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1. Single-cell nascent RNA sequencing unveils coordinated global transcription

   https://doi.org/10.1038/s41586-024-07517-7  

   Mahat, Dig B; Tippens, Nathaniel D; Martin-Rufino, Jorge D; Waterton, Sean K; Fu, Jiayu; Blatt, Sarah E; Sharp, Phillip A (July 2024, Nature)

   Abstract Transcription is the primary regulatory step in gene expression. Divergent transcription initiation from promoters and enhancers produces stable RNAs from genes and unstable RNAs from enhancers^1,2. Nascent RNA capture and sequencing assays simultaneously measure gene and enhancer activity in cell populations³. However, fundamental questions about the temporal regulation of transcription and enhancer–gene coordination remain unanswered, primarily because of the absence of a single-cell perspective on active transcription. In this study, we present scGRO–seq—a new single-cell nascent RNA sequencing assay that uses click chemistry—and unveil coordinated transcription throughout the genome. We demonstrate the episodic nature of transcription and the co-transcription of functionally related genes. scGRO–seq can estimate burst size and frequency by directly quantifying transcribing RNA polymerases in individual cells and can leverage replication-dependent non-polyadenylated histone gene transcription to elucidate cell cycle dynamics. The single-nucleotide spatial and temporal resolution of scGRO–seq enables the identification of networks of enhancers and genes. Our results suggest that the bursting of transcription at super-enhancers precedes bursting from associated genes. By imparting insights into the dynamic nature of global transcription and the origin and propagation of transcription signals, we demonstrate the ability of scGRO–seq to investigate the mechanisms of transcription regulation and the role of enhancers in gene expression. 

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2. Enhancer Pleiotropy, Gene Expression, and the Architecture of Human Enhancer–Gene Interactions

   https://doi.org/10.1093/molbev/msab085  

   Singh, Devika; Yi, Soojin V (March 2021, Molecular Biology and Evolution)

   Saitou, Naruya (Ed.)

   Abstract Enhancers are often studied as noncoding regulatory elements that modulate the precise spatiotemporal expression of genes in a highly tissue-specific manner. This paradigm has been challenged by recent evidence of individual enhancers acting in multiple tissues or developmental contexts. However, the frequency of these enhancers with high degrees of “pleiotropy” out of all putative enhancers is not well understood. Consequently, it is unclear how the variation of enhancer pleiotropy corresponds to the variation in expression breadth of target genes. Here, we use multi-tissue chromatin maps from diverse human tissues to investigate the enhancer–gene interaction architecture while accounting for 1) the distribution of enhancer pleiotropy, 2) the variations of regulatory links from enhancers to target genes, and 3) the expression breadth of target genes. We show that most enhancers are tissue-specific and that highly pleiotropy enhancers account for <1% of all putative regulatory sequences in the human genome. Notably, several genomic features are indicative of increasing enhancer pleiotropy, including longer sequence length, greater number of links to genes, increasing abundance and diversity of encoded transcription factor motifs, and stronger evolutionary conservation. Intriguingly, the number of enhancers per gene remains remarkably consistent for all genes (∼14). However, enhancer pleiotropy does not directly translate to the expression breadth of target genes. We further present a series of Gaussian Mixture Models to represent this organization architecture. Consequently, we demonstrate that a modest trend of more pleiotropic enhancers targeting more broadly expressed genes can generate the observed diversity of expression breadths in the human genome. 

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3. Long G4-rich enhancers target promoters via a G4 DNA-based mechanism

   https://doi.org/10.1093/nar/gkae1180  

   DeMeis, Jeffrey D.; Roberts, Justin T.; Delcher, Haley A.; Godang, Noel L.; Coley, Alexander B.; Brown, Cana L.; Shaw, Michael H.; Naaz, Sayema; Dahal, Ayush; Alqudah, Shahem Y.; et al (December 2024, Nucleic Acids Research)

   Abstract Several studies have now described instances where G-rich sequences in promoters and enhancers regulate gene expression through forming G-quadruplex (G4) structures. Relatedly, our group recently identified 301 long genomic stretches significantly enriched for minimal G4 motifs (LG4s) in humans and found the majority of these overlap annotated enhancers, and furthermore, that the promoters regulated by these LG4 enhancers are similarly enriched with G4-capable sequences. While the generally accepted model for enhancer:promoter specificity maintains that interactions are dictated by enhancer- and promoter-bound transcriptional activator proteins, the current study tested an alternative hypothesis: that LG4 enhancers interact with cognate promoters via a direct G4:G4 DNA-based mechanism. This work establishes the nuclear proximity of LG4 enhancer:promoter pairs, biochemically demonstrates the ability of individual LG4 single-stranded DNAs (ssDNAs) to directly interact target promoter ssDNAs, and confirms that these interactions, as well as the ability of LG4 enhancers to activate target promoters in culture, are mediated by G4 DNA. 

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4. Relating enhancer genetic variation across mammals to complex phenotypes using machine learning

   https://doi.org/10.1126/science.abm7993  

   Kaplow, Irene M.; Lawler, Alyssa J.; Schäffer, Daniel E.; Srinivasan, Chaitanya; Sestili, Heather H.; Wirthlin, Morgan E.; Phan, BaDoi N.; Prasad, Kavya; Brown, Ashley R.; Zhang, Xiaomeng; et al (April 2023, Science)

   INTRODUCTION Diverse phenotypes, including large brains relative to body size, group living, and vocal learning ability, have evolved multiple times throughout mammalian history. These shared phenotypes may have arisen repeatedly by means of common mechanisms discernible through genome comparisons. RATIONALE Protein-coding sequence differences have failed to fully explain the evolution of multiple mammalian phenotypes. This suggests that these phenotypes have evolved at least in part through changes in gene expression, meaning that their differences across species may be caused by differences in genome sequence at enhancer regions that control gene expression in specific tissues and cell types. Yet the enhancers involved in phenotype evolution are largely unknown. Sequence conservation–based approaches for identifying such enhancers are limited because enhancer activity can be conserved even when the individual nucleotides within the sequence are poorly conserved. This is due to an overwhelming number of cases where nucleotides turn over at a high rate, but a similar combination of transcription factor binding sites and other sequence features can be maintained across millions of years of evolution, allowing the function of the enhancer to be conserved in a particular cell type or tissue. Experimentally measuring the function of orthologous enhancers across dozens of species is currently infeasible, but new machine learning methods make it possible to make reliable sequence-based predictions of enhancer function across species in specific tissues and cell types. RESULTS To overcome the limits of studying individual nucleotides, we developed the Tissue-Aware Conservation Inference Toolkit (TACIT). Rather than measuring the extent to which individual nucleotides are conserved across a region, TACIT uses machine learning to test whether the function of a given part of the genome is likely to be conserved. More specifically, convolutional neural networks learn the tissue- or cell type–specific regulatory code connecting genome sequence to enhancer activity using candidate enhancers identified from only a few species. This approach allows us to accurately associate differences between species in tissue or cell type–specific enhancer activity with genome sequence differences at enhancer orthologs. We then connect these predictions of enhancer function to phenotypes across hundreds of mammals in a way that accounts for species’ phylogenetic relatedness. We applied TACIT to identify candidate enhancers from motor cortex and parvalbumin neuron open chromatin data that are associated with brain size relative to body size, solitary living, and vocal learning across 222 mammals. Our results include the identification of multiple candidate enhancers associated with brain size relative to body size, several of which are located in linear or three-dimensional proximity to genes whose protein-coding mutations have been implicated in microcephaly or macrocephaly in humans. We also identified candidate enhancers associated with the evolution of solitary living near a gene implicated in separation anxiety and other enhancers associated with the evolution of vocal learning ability. We obtained distinct results for bulk motor cortex and parvalbumin neurons, demonstrating the value in applying TACIT to both bulk tissue and specific minority cell type populations. To facilitate future analyses of our results and applications of TACIT, we released predicted enhancer activity of >400,000 candidate enhancers in each of 222 mammals and their associations with the phenotypes we investigated. CONCLUSION TACIT leverages predicted enhancer activity conservation rather than nucleotide-level conservation to connect genetic sequence differences between species to phenotypes across large numbers of mammals. TACIT can be applied to any phenotype with enhancer activity data available from at least a few species in a relevant tissue or cell type and a whole-genome alignment available across dozens of species with substantial phenotypic variation. Although we developed TACIT for transcriptional enhancers, it could also be applied to genomic regions involved in other components of gene regulation, such as promoters and splicing enhancers and silencers. As the number of sequenced genomes grows, machine learning approaches such as TACIT have the potential to help make sense of how conservation of, or changes in, subtle genome patterns can help explain phenotype evolution. Tissue-Aware Conservation Inference Toolkit (TACIT) associates genetic differences between species with phenotypes. TACIT works by generating open chromatin data from a few species in a tissue related to a phenotype, using the sequences underlying open and closed chromatin regions to train a machine learning model for predicting tissue-specific open chromatin and associating open chromatin predictions across dozens of mammals with the phenotype. [Species silhouettes are from PhyloPic] 

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5. Global patterns of enhancer activity during sea urchin embryogenesis assessed by eRNA profiling

   https://doi.org/10.1101/gr.275684.121  

   Khor, Jian Ming; Guerrero-Santoro, Jennifer; Douglas, William; Ettensohn, Charles A. (September 2021, Genome Research)

   We used capped analysis of gene expression with sequencing (CAGE-seq) to profile eRNA expression and enhancer activity during embryogenesis of a model echinoderm: the sea urchin, Strongylocentrotus purpuratus . We identified more than 18,000 enhancers that were active in mature oocytes and developing embryos and documented a burst of enhancer activation during cleavage and early blastula stages. We found that a large fraction (73.8%) of all enhancers active during the first 48 h of embryogenesis were hyperaccessible no later than the 128-cell stage and possibly even earlier. Most enhancers were located near gene bodies, and temporal patterns of eRNA expression tended to parallel those of nearby genes. Furthermore, enhancers near lineage-specific genes contained signatures of inputs from developmental gene regulatory networks deployed in those lineages. A large fraction (60%) of sea urchin enhancers previously shown to be active in transgenic reporter assays was associated with eRNA expression. Moreover, a large fraction (50%) of a representative subset of enhancers identified by eRNA profiling drove tissue-specific gene expression in isolation when tested by reporter assays. Our findings provide an atlas of developmental enhancers in a model sea urchin and support the utility of eRNA profiling as a tool for enhancer discovery and regulatory biology. The data generated in this study are available at Echinobase, the public database of information related to echinoderm genomics. 

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