Functional Regulatory Elements Research Articles

CRISPR-CLEAR: Nucleotide-Resolution Mapping of Regulatory Elements via Allelic Readout of Tiled Base Editing.

CRISPR tiling screens have advanced the identification and characterization of regulatory sequences but are limited by low resolution arising from the indirect readout of editing via guide RNA sequencing. This study introduces CRISPR-CLEAR , an end-to-end experimental assay and computational pipeline, which leverages targeted sequencing of CRISPR-introduced alleles at the endogenous target locus following dense base-editing mutagenesis. This approach enables the dissection of regulatory elements at nucleotide resolution, facilitating a direct assessment of genotype-phenotype effects.

Uncovering methylation-dependent genetic effects on regulatory element function in diverse genomes.

A major goal in evolutionary biology and biomedicine is to understand the complex interactions between genetic variants, the epigenome, and gene expression. However, the causal relationships between these factors remain poorly understood. mSTARR-seq, a methylation-sensitive massively parallel reporter assay, is capable of identifying methylation-dependent regulatory activity at many thousands of genomic regions simultaneously, and allows for the testing of causal relationships between DNA methylation and gene expression on a region-by-region basis. Here, we developed a multiplexed mSTARR-seq protocol to assay naturally occurring human genetic variation from 25 individuals sampled from 10 localities in Europe and Africa. We identified 6,957 regulatory elements in either the unmethylated or methylated state, and this set was enriched for enhancer and promoter annotations, as expected. The expression of 58% of these regulatory elements was modulated by methylation, which was generally associated with decreased RNA expression. Within our set of regulatory elements, we used allele-specific expression analyses to identify 8,020 sites with genetic effects on gene regulation; further, we found that 42.3% of these genetic effects varied between methylated and unmethylated states. Sites exhibiting methylation-dependent genetic effects were enriched for GWAS and EWAS annotations, implicating them in human disease. Compared to datasets that assay DNA from a single European individual, our multiplexed assay uncovers dramatically more genetic effects and methylation-dependent genetic effects, highlighting the importance of including diverse individuals in assays which aim to understand gene regulatory processes.

Liver eQTL meta-analysis illuminates potential molecular mechanisms of cardiometabolic traits

Understanding the molecular mechanisms of complex traits is essential for developing targeted interventions. We analyzed liver expression quantitative-trait locus (eQTL) meta-analysis data on 1,183 participants to identify conditionally distinct signals. We found 9,013 eQTL signals for 6,564 genes; 23% of eGenes had two signals, and 6% had three or more signals. We then integrated the eQTL results with data from 29 cardiometabolic genome-wide association study (GWAS) traits and identified 1,582 GWAS-eQTL colocalizations for 747 eGenes. Non-primary eQTL signals accounted for 17% of all colocalizations. Isolating signals by conditional analysis prior to coloc resulted in 37% more colocalizations than using marginal eQTL and GWAS data, highlighting the importance of signal isolation. Isolating signals also led to stronger evidence of colocalization: among 343 eQTL-GWAS signal pairs in multi-signal regions, analyses that isolated the signals of interest resulted in higher posterior probability of colocalization for 41% of tests. Leveraging allelic heterogeneity, we predicted causal effects of gene expression on liver traits for four genes. To predict functional variants and regulatory elements, we colocalized eQTL with liver chromatin accessibility QTL (caQTL) and found 391 colocalizations, including 73 with non-primary eQTL signals and 60 eQTL signals that colocalized with both a caQTL and a GWAS signal. Finally, we used publicly available massively parallel reporter assays in HepG2 to highlight 14 eQTL signals that include at least one expression-modulating variant. This multi-faceted approach to unraveling the genetic underpinnings of liver-related traits could lead to therapeutic development.

RNA thermometers are widespread upstream of ABC transporter genes in bacteria

RNA thermometers are temperature-sensing non-coding RNAs that regulate the expression of downstream genes. A well-characterized RNA thermometer motif discovered in bacteria is the ROSE-like element (repression of heat shock gene expression). ATP-binding cassette (ABC) transporters are a superfamily of transmembrane proteins that harness ATP hydrolysis to facilitate the export and import of substrates across cellular membranes. Through structure-guided bioinformatics, we discovered that ROSE-like RNA thermometers are widespread upstream of ABC transporter genes in bacteria. X-ray crystallography, biochemistry, and cellular assays indicate that these RNA thermometers are functional regulatory elements. This study expands the known biological role of RNA thermometers to these key membrane transporters.

Variants in tubule epithelial regulatory elements mediate most heritable differences in human kidney function.

Kidney disease is highly heritable; however, the causal genetic variants, the cell types in which these variants function, and the molecular mechanisms underlying kidney disease remain largely unknown. To identify genetic loci affecting kidney function, we performed a GWAS using multiple kidney function biomarkers and identified 462 loci. To begin to investigate how these loci affect kidney function, we generated single-cell chromatin accessibility (scATAC-seq) maps of the human kidney and identified candidate cis-regulatory elements (cCREs) for kidney podocytes, tubule epithelial cells, and kidney endothelial, stromal, and immune cells. Kidney tubule epithelial cCREs explained 58% of kidney function SNP-heritability and kidney podocyte cCREs explained an additional 6.5% of SNP-heritability. In contrast, little kidney function heritability was explained by kidney endothelial, stromal, or immune cell-specific cCREs. Through functionally informed fine-mapping, we identified putative causal kidney function variants and their corresponding cCREs. Using kidney scATAC-seq data, we created a deep learning model (which we named ChromKid) to predict kidney cell type-specific chromatin accessibility from sequence. ChromKid and allele specific kidney scATAC-seq revealed that many fine-mapped kidney function variants locally change chromatin accessibility in tubule epithelial cells. Enhancer assays confirmed that fine-mapped kidney function variants alter tubule epithelial regulatory element function. To map the genes which these regulatory elements control, we used CRISPR interference (CRISPRi) to target these regulatory elements in tubule epithelial cells and assessed changes in gene expression. CRISPRi of enhancers harboring kidney function variants regulated NDRG1 and RBPMS expression. Thus, inherited differences in tubule epithelial NDRG1 and RBPMS expression may predispose to kidney disease in humans. We conclude that genetic variants affecting tubule epithelial regulatory element function account for most SNP-heritability of human kidney function. This work provides an experimental approach to identify the variants, regulatory elements, and genes involved in polygenic disease.

SpRY-mediated screens facilitate functional dissection of non-coding sequences at single-base resolution

CRISPR mutagenesis screens conducted with SpCas9 and other nucleases have identified certain cis-regulatory elements and genetic variants but at a limited resolution due to the absence of protospacer adjacent motif (PAM) sequences. Here, leveraging the broad targeting scope of the near-PAMless SpRY variant, we have demonstrated that saturated SpRY mutagenesis and base editing screens can faithfully identify functional regulatory elements and essential genetic variants for target gene expression at single-base resolution. We further extended this methodology to investigate a genome-wide association study (GWAS) locus at 10q22.1 associated with a red blood cell trait, where we identified potential enhancers regulating HK1 gene expression, despite not all of these enhancers exhibiting typical chromatin signatures. More importantly, our saturated base editing screens pinpoint multiple causal variants within this locus that would otherwise be missed by Bayesian statistical fine-mapping. Our approach is generally applicable to functional interrogation of all non-coding genomic elements while complementing other high-coverage CRISPR screens.

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.

Massively parallel characterization of regulatory elements in the developing human cortex.

Nucleotide changes in gene regulatory elements are important determinants of neuronal development and diseases. Using massively parallel reporter assays in primary human cells from mid-gestation cortex and cerebral organoids, we interrogated the cis-regulatory activity of 102,767 open chromatin regions, including thousands of sequences with cell type-specific accessibility and variants associated with brain gene regulation. In primary cells, we identified 46,802 active enhancer sequences and 164 variants that alter enhancer activity. Activity was comparable in organoids and primary cells, suggesting that organoids provide an adequate model for the developing cortex. Using deep learning we decoded the sequence basis and upstream regulators of enhancer activity. This work establishes a comprehensive catalog of functional gene regulatory elements and variants in human neuronal development.

Genome-wide Cas9-mediated screening of essential non-coding regulatory elements via libraries of paired single-guide RNAs

The functions of non-coding regulatory elements (NCREs), which constitute a major fraction of the human genome, have not been systematically studied. Here we report a method involving libraries of paired single-guide RNAs targeting both ends of an NCRE as a screening system for the Cas9-mediated deletion of thousands of NCREs genome-wide to study their functions in distinct biological contexts. By using K562 and 293T cell lines and human embryonic stem cells, we show that NCREs can have redundant functions, and that many ultra-conserved elements have silencer activity and play essential roles in cell growth and in cellular responses to drugs (notably, the ultra-conserved element PAX6_Tarzan may be critical for heart development, as removing it from human embryonic stem cells led to defects in cardiomyocyte differentiation). The high-throughput screen, which is compatible with single-cell sequencing, may allow for the identification of druggable NCREs.

The potential and pitfalls of large language models in molecular biosciences

Large language models (LLMs) have revolutionized information processing and are now being applied to complex problems in molecular biosciences. LLMs excel at learning patterns in biological sequences, enabling applications such as predicting the function of DNA regulatory elements, analysing chromatin modifications and predicting the effects of genetic variants. In drug discovery, LLMs are being used to optimize drug properties, design novel molecules and even automate compound synthesis. However, realizing the full potential of LLMs in the molecular biosciences requires addressing challenges such as designing effective learning objectives, generating high-quality, domain-specific datasets and building trust in AI-assisted decision-making. As these challenges are addressed, LLMs hold immense potential for advancing research and driving ground-breaking discoveries in molecular biosciences.

MNESEDA: A prior-guided subgraph representation learning framework for predicting disease-related enhancers

Enhancers, as a class of functional genomic regulatory elements, activate transcription of their target genes and play significant roles in the pathogenesis of complex human diseases. Therefore, identifying associations between enhancers and diseases could help to illuminate the potential disease pathogenesis mechanism. However, current methods for identifying disease-related enhancers mainly focus on biological experiments, which are time-consuming and cost-intensive. It is crucial to develop an effective computational model for detecting novel enhancer-disease associations (EDAs). To this end, we propose a prior-guided maximum neighbor enclosing subgraph (MNES) representation learning framework named MNESEDA, for enhancer-disease association prediction, where maximum connected motif (MCM) discovery is a key ingredient that constrains the subsequent MNES extraction strategy. The proposed residual information fusion captures representation difference between motif subgraphs and alleviates the over-smoothing issue. Extensive experiments conducted on three datasets demonstrate the superior performance of MNESEDA over recent state-of-the-art methods and the satisfactory generalization and transfer ability. Further case studies exhibit the potential ability of MNESEDA to predict novel disease-related enhancers. Overall, MNESEDA could serve as an effective guide to elucidate pathogenesis and etiology of human diseases and decipher potential disease biomarkers.

Human gene regulatory evolution is driven by the divergence of regulatory element function in both cis and trans

Gene regulatory divergence between species can result from cis-acting local changes to regulatory element DNA sequences or global trans-acting changes to the regulatory environment. Understanding how these mechanisms drive regulatory evolution has been limited by challenges in identifying trans-acting changes. We present a comprehensive approach to directly identify cis- and trans-divergent regulatory elements between human and rhesus macaque lymphoblastoid cells using assay for transposase-accessible chromatin coupled to self-transcribing active regulatory region (ATAC-STARR) sequencing. In addition to thousands of cis changes, we discover an unexpected number (∼10,000) of trans changes and show that cis and trans elements exhibit distinct patterns of sequence divergence and function. We further identify differentially expressed transcription factors that underlie ∼37% of trans differences and trace how cis changes can produce cascades of trans changes. Overall, we find that most divergent elements (67%) experienced changes in both cis and trans, revealing a substantial role for trans divergence-alone and together with cis changes-in regulatory differences between species.

Abstract 6146: Characterizing underlying molecular mechanisms of prostate cancer risk variants identified from multi-ethnic GWAS

Abstract Prostate cancer (PCa) is a complex disease with the second leading cause of cancer death in US males. Multi-ethnic genome-wide association studies (GWAS) identified a thousand genetic variants associated with PCa risk in different races. However, the majority of risk variants are located in non-coding regions, making it challenging to understand their contribution to PCa risk. Non-coding regions include functional regulatory elements (REs) such as promoters and enhancers, whose activity can be evaluated by histone modification H3K4me3 and H3K27ac, respectively. In this study, we integrated PCa risk variants with hundreds of H3K4me3 and H3K27ac ChIP-seq data from normal and cancer prostate cell lines and tissues, finding 122 variants in promoters and 301 variants in enhancers. Enhancers are distal to promoters, and they form loops to regulate gene expression, mediated by transcription factors (TFs). Enhancer activities are tightly associated with cell fate. Genetic variants can affect the activity of enhancers by changing the binding preference of TFs and such changes can affect chromatin interactions and dysregulate gene expression. We hypothesize that allele changes of enhancer variants contribute to PCa risk differently among individuals from different ancestries, leading to the development of heterogeneous PCa molecular subgroups, characterized by genetic alterations including TMPRSS2-ERG fusion. By integrating H3K27ac ChIP-seq data from prostate cell lines derived from European (EUR) and African (AFR) ancestries, we found 185 risk variants in enhancers in EUR cell lines and 69 risk variants in enhancers in AFR cell lines. Next, we integrated patient tissue-derived H3K27ac ChIP-seq data and performed chromatin quantitative trait loci (cQTL) and allelic imbalance (AI) analyses to determine risk variants associated with enhancer activity. Among the 185 risk enhancer variants in EUR and 69 in AFR cell lines, we found 130 and 46 with significant association, respectively. To determine TFs that change binding upon allele change of enhancer risk variants, we integrated hundreds of TF ChIP-seq data from prostate cell lines and tissues. Among the 185 risk enhancer variants in EUR and 69 in AFR cell lines, we found 142 and 44 variants bound by TFs, respectively. When we further performed cQTL and AI analyses for FOXA1, HOXB13, and AR, we found 87 and 29 variants significantly associated with TF binding affinity, respectively. Moreover, we found that these associations differ between TMPRSS2-ERG fusion positive and negative tumors. To identify the target genes of enhancer risk variants, we are currently integrating chromatin interaction data including Hi-C and Micro-C. Overall, this study provides valuable insights into the underlying molecular mechanisms of risk variants in various populations and individuals which lead to the development of heterogeneous PCa subgroups. Citation Format: Zexun Wu, Andrew Vu, Ethan S. Nelson-Moore, Huan Cao, Suhn K. Rhie. Characterizing underlying molecular mechanisms of prostate cancer risk variants identified from multi-ethnic GWAS [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6146.

Identification of 27 allele-specific regulatory variants in Parkinson's disease using a massively parallel reporter assay.

Genome wide association studies (GWAS) have identified a number of genomic loci that are associated with Parkinson's disease (PD) risk. However, the majority of these variants lie in non-coding regions, and thus the mechanisms by which they influence disease development, and/or potential subtypes, remain largely elusive. To address this, we used a massively parallel reporter assay (MPRA) to screen the regulatory function of 5254 variants that have a known or putative connection to PD. We identified 138 loci with enhancer activity, of which 27 exhibited allele-specific regulatory activity in HEK293 cells. The identified regulatory variant(s) typically did not match the original tag variant within the PD associated locus, supporting the need for deeper exploration of these loci. The existence of allele specific transcriptional impacts within HEK293 cells, confirms that at least a subset of the PD associated regions mark functional gene regulatory elements. Future functional studies that confirm the putative targets of the empirically verified regulatory variants will be crucial for gaining a greater understanding of how gene regulatory network(s) modulate PD risk.

Heritable, multinucleotide deletions in plants using viral delivery of a repair exonuclease and guide RNAs.

CRISPR/Cas9-mediated mutagenesis typically results in short insertion/deletion mutations, which are often too small to disrupt the function of cis-acting regulatory elements. Here, we describe a highly efficient in planta gene editing approach called VirTREX2-HLDel that achieves heritable multinucleotide deletions in both protein-coding genes and noncoding DNA regulatory elements. VirTREX2-HLDel uses RNA viruses to deliver both the 3 prime repair exonuclease 2 (TREX2) and single-guide RNAs. Our method enables recovery of multiplexed heritable deletions and increases the heritable gene editing frequency at poorly edited sites. We identified functional conservation and divergence of MICRORNA164 (miR164) in Nicotiana benthamiana and tomato (Solanum lycopersicum) using VirTREX2-HLDel and observed previously uncharacterized phenotypes in plants with large deletions at this locus. Our viral delivery method reduces the need for tissue culture and will accelerate the understanding of protein-coding and regulatory regions in plants.

Exploring the Function of Gene Promoter Regulatory Elements Using CRISPR Tools.

Gene promoters serve as pivotal regulators of transcription, orchestrating the initiation, rate, and specificity of gene expression, resulting in cellular diversity found among distinct cell types within multicellular organisms. Identification of the sequence and function of promoters' regulatory elements and their complex interaction with transcription factors, enhancers, silencers, and insulators is fundamental to coordinated transcriptional processes within cells. Identifying these regulatory elements and scrutinizing their functions and interactions through the use of synthetic promoters can pave the way for researchers in various fields ranging from uncovering the origins of diseases associated with promoter mutations to harnessing these regulatory components in biotechnological applications.In this chapter, we describe the manipulation of regulatory elements within promoters, with a specific focus on the use of CRISPR technology on enhancers and silencer elements of the Ovalbumin gene promoter. We explain and discuss processes for the deletion of/interference with regulatory elements within the promoter, employing CRISPR-based approaches. Furthermore, we demonstrate that a CRISPR/Cas-manipulated promoter can activate gene transcription in cell types where it is normally inactive. This confirms that CRISPR technology can be effectively used to engineer synthetic promoters with desired characteristics, such as inducibility, tissue-specificity, or enhanced transcriptional strength. Such an approach provides valuable insights into the mechanisms and dynamics of gene expression, thereby offering new opportunities in the fields of biotechnology and medicine.

Role of Genetic Variation in Transcriptional Regulatory Elements in Heart Rhythm.

Genetic predisposition to cardiac arrhythmias has been a field of intense investigation. Research initially focused on rare hereditary arrhythmias, but over the last two decades, the role of genetic variation (single nucleotide polymorphisms) in heart rate, rhythm, and arrhythmias has been taken into consideration as well. In particular, genome-wide association studies have identified hundreds of genomic loci associated with quantitative electrocardiographic traits, atrial fibrillation, and less common arrhythmias such as Brugada syndrome. A significant number of associated variants have been found to systematically localize in non-coding regulatory elements that control the tissue-specific and temporal transcription of genes encoding transcription factors, ion channels, and other proteins. However, the identification of causal variants and the mechanism underlying their impact on phenotype has proven difficult due to the complex tissue-specific, time-resolved, condition-dependent, and combinatorial function of regulatory elements, as well as their modest conservation across different model species. In this review, we discuss research efforts aimed at identifying and characterizing-trait-associated variant regulatory elements and the molecular mechanisms underlying their impact on heart rate or rhythm.

Peak-agnostic high-resolution cis-regulatory circuitry mapping using single cell multiome data.

Single same cell RNAseq/ATACseq multiome data provide unparalleled potential to develop high resolution maps of the cell-type specific transcriptional regulatory circuitry underlying gene expression. We present CREMA, a framework that recovers the full cis-regulatory circuitry by modeling gene expression and chromatin activity in individual cells without peak-calling or cell type labeling constraints. We demonstrate that CREMA overcomes the limitations of existing methods that fail to identify about half of functional regulatory elements which are outside the called chromatin 'peaks'. These circuit sites outside called peaks are shown to be important cell type specific functional regulatory loci, sufficient to distinguish individual cell types. Analysis of mouse pituitary data identifies a Gata2-circuit for the gonadotrope-enriched disease-associated Pcsk1 gene, which is experimentally validated by reduced gonadotrope expression in a gonadotrope conditional Gata2-knockout model. We present a web accessible human immune cell regulatory circuit resource, and provide CREMA as an R package.

A Cre-dependent massively parallel reporter assay allows for cell-type specific assessment of the functional effects of non-coding elements in vivo

The function of regulatory elements is highly dependent on the cellular context, and thus for understanding the function of elements associated with psychiatric diseases these would ideally be studied in neurons in a living brain. Massively Parallel Reporter Assays (MPRAs) are molecular genetic tools that enable functional screening of hundreds of predefined sequences in a single experiment. These assays have not yet been adapted to query specific cell types in vivo in a complex tissue like the mouse brain. Here, using a test-case 3′UTR MPRA library with genomic elements containing variants from autism patients, we developed a method to achieve reproducible measurements of element effects in vivo in a cell type-specific manner, using excitatory cortical neurons and striatal medium spiny neurons as test cases. This targeted technique should enable robust, functional annotation of genetic elements in the cellular contexts most relevant to psychiatric disease.

Characterization of transcriptional enhancers in the chicken genome using CRISPR-mediated activation

DNA regulatory elements intricately control when, where, and how genes are activated. Therefore, understanding the function of these elements could unveil the complexity of the genetic regulation network. Genome-wide significant variants are predominantly found in non-coding regions of DNA, so comprehending the predicted functional regulatory elements is crucial for understanding the biological context of these genomic markers, which can be incorporated into breeding programs. The emergence of CRISPR technology has provided a powerful tool for studying non-coding regulatory elements in genomes. In this study, we leveraged epigenetic data from the Functional Annotation of Animal Genomes project to identify promoter and putative enhancer regions associated with three genes (HBBA, IRF7, and PPARG) in the chicken genome. To identify the enhancer regions, we designed guide RNAs targeting the promoter and candidate enhancer regions and utilized CRISPR activation (CRISPRa) with dCas9-p300 and dCas9-VPR as transcriptional activators in chicken DF-1 cells. By comparing the expression levels of target genes between the promoter activation and the co-activation of the promoter and putative enhancers, we were able to identify functional enhancers that exhibited augmented upregulation. In conclusion, our findings demonstrate the remarkable efficiency of CRISPRa in precisely manipulating the expression of endogenous genes by targeting regulatory elements in the chicken genome, highlighting its potential for functional validation of non-coding regions.