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Transcriptional regulation of gene expression is a major mechanism used by plants to confer phenotypic plasticity, and yet compared with other eukaryotes or bacteria, little is known about the design principles. We generated an extensive catalog of nascent and steady-state transcripts in Arabidopsis thaliana seedlings using global nuclear run-on sequencing (GRO-seq), 5′GRO-seq, and RNA-seq and reanalyzed published maize data to capture characteristics of plant transcription. De novo annotation of nascent transcripts accurately mapped start sites and unstable transcripts. Examining the promoters of coding and noncoding transcripts identified comparable chromatin signatures, a conserved “TGT” core promoter motif and unreported transcription factor-binding sites. Mapping of engaged RNA polymerases showed a lack of enhancer RNAs, promoter-proximal pausing, and divergent transcription in Arabidopsis seedlings and maize, which are commonly present in yeast and humans. In contrast, Arabidopsis and maize genes accumulate RNA polymerases in proximity of the polyadenylation site, a trend that coincided with longer genes and CpG hypomethylation. Lack of promoter-proximal pausing and a higher correlation of nascent and steady-state transcripts indicate Arabidopsis may regulate transcription predominantly at the level of initiation. Our findings provide insight into plant transcription and eukaryotic gene expression as a whole.

Steady-state gene expression across the cell cycle has been studied extensively. However, transcriptional gene regulation and the dynamics of histone modification at different cell-cycle stages are largely unknown. By applying a combination of global nuclear run-on sequencing (GRO-seq), RNA sequencing (RNA-seq), and histone-modification Chip sequencing (ChIP-seq), we depicted a comprehensive transcriptional landscape at the G0/G1, G1/S, and M phases of breast cancer MCF-7 cells. Importantly, GRO-seq and RNA-seq analysis identified different cell-cycle–regulated genes, suggesting a lag between transcription and steady-state expression during the cell cycle. Interestingly, we identified genes actively transcribed at early M phase that are longer in length and have low expression and are accompanied by a global increase in active histone 3 lysine 4 methylation (H3K4me2) and histone 3 lysine 27 acetylation (H3K27ac) modifications. In addition, we identified 2,440 cell-cycle–regulated enhancer RNAs (eRNAs) that are strongly associated with differential active transcription but not with stable expression levels across the cell cycle. Motif analysis of dynamic eRNAs predicted Kruppel-like factor 4 (KLF4) as a key regulator of G1/S transition, and this identification was validated experimentally. Taken together, our combined analysis characterized the transcriptional and histone-modification profile of the human cell cycle and identified dynamic transcriptional signatures across the cell cycle.

Enhancers are critical genomic elements that define cellular and functional identity through the spatial and temporal regulation of gene expression. Recent studies suggest that key genes regulating cell type-specific functions reside in enhancer-dense genomic regions (i.e., super enhancers, stretch enhancers). Here we report that enhancer RNAs (eRNAs) identified by global nuclear run-on sequencing are extensively transcribed within super enhancers and are dynamically regulated in response to cellular signaling. Using Toll-like receptor 4 (TLR4) signaling in macrophages as a model system, we find that transcription of super enhancer-associated eRNAs is dynamically induced at most of the key genes driving innate immunity and inflammation. Unexpectedly, genes repressed by TLR4 signaling are also associated with super enhancer domains and accompanied by massive repression of eRNA transcription. Furthermore, we find each super enhancer acts as a single regulatory unit within which eRNA and genic transcripts are coordinately regulated. The key regulatory activity of these domains is further supported by the finding that super enhancer-associated transcription factor binding is twice as likely to be conserved between human and mouse than typical enhancer sites. Our study suggests that transcriptional activities at super enhancers are critical components to understand the dynamic gene regulatory network. Significance Super enhancers (SEs) are enhancer-dense regions found near genes that play key roles in determining cellular identity. Using global nuclear run-on sequencing (GRO-Seq), we find extensive regulation of enhancer RNAs (eRNAs) within SEs in response to lipopolysaccharide (LPS) treatment in macrophages. Both activation and repression of gene expression are associated with SEs and eRNA transcription dynamics. Furthermore, we find that each SE acts as a single regulatory unit within which eRNA and genic transcripts are coordinately regulated. We also find that transcription factor (TF) composition within an SE determines regulatory properties of each SE and associated eRNAs. We propose that signal-dependent SEs and their eRNAs function as molecular rheostats integrating the binding profiles of key regulators to produce dynamic profiles of gene expression.

Production of mRNA depends critically on the rate of RNA polymerase II (Pol II) elongation. To dissect Pol II dynamics in mouse ES cells, we inhibited Pol II transcription at either initiation or promoter-proximal pause escape with Triptolide or Flavopiridol, and tracked Pol II kinetically using GRO-seq. Both inhibitors block transcription of more than 95% of genes, showing that pause escape, like initiation, is a ubiquitous and crucial step within the transcription cycle. Moreover, paused Pol II is relatively stable, as evidenced from half-life measurements at ∼3200 genes. Finally, tracking the progression of Pol II after drug treatment establishes Pol II elongation rates at over 1000 genes. Notably, Pol II accelerates dramatically while transcribing through genes, but slows at exons. Furthermore, intergenic variance in elongation rates is substantial, and is influenced by a positive effect of H3K79me2 and negative effects of exon density and CG content within genes. Many different factors determine how quickly the DNA in genes is transcribed to produce molecules of messenger RNA. The start of the transcription process features two milestones: first, an enzyme called RNA Polymerase II starts the process; shortly afterwards, however, the process pauses and only starts again when other proteins are recruited. This provides two levels of control over the production of messenger RNA and, it also allows the transcription process to be interrupted in order to study the rate of transcription. Here, Jonkers, Kwak and Lis used two drugs to block either the start of transcription or the release from the paused state in mouse cells. Both drugs prevented new transcription and disrupted about 95% of the total number of genes. However, RNA Polymerase II that was already copying DNA could continue to copy, and did so at an average rate of 2000 bases per minute. Transcription rates were, however, shown to vary between different genes—highly active genes are transcribed faster. Transcription rates also varied within individual genes, with the enzyme accelerating as it moves along the gene. This suggests that the transcription machinery, including other proteins that improve the enzyme’s efficiency, are recruited or modified after transcription has already started, and that these proteins help the enzyme to reach its maximum transcription speed. Other factors also affected the transcription rate: the genetic code is written in four letters—A, C, G and T—and genes that contained more Cs and Gs were transcribed slower than those with lots of As and Ts. Genes also contain regions called exons that code for proteins, and regions called introns that do not: Jonkers, Kwak and Lis found that genes with lots of exons were transcribed slower. Furthermore, DNA is wrapped around proteins into a compacted structure, and genes that had certain chemical markings added to these proteins were transcribed faster. The work of Jonkers, Kwak and Lis is the first in-depth look at how transcription is affected by gene structure, and leads the way to uncovering how transcription rates throughout genes are regulated to influence production of messenger RNA.

Recent sequencing technologies enable joint quantification of promoters and their enhancer regions, allowing inference of enhancer–promoter links. We show that current enhancer–promoter inference methods produce a high rate of false positive links. We introduce FOCS, a new inference method, and by benchmarking against ChIA-PET, HiChIP, and eQTL data show that it results in lower false discovery rates and at the same time higher inference power. By applying FOCS to 2630 samples taken from ENCODE, Roadmap Epigenomics, FANTOM5, and a new compendium of GRO-seq samples, we provide extensive enhancer–promotor maps ( http://acgt.cs.tau.ac.il/focs ). We illustrate the usability of our maps for deriving biological hypotheses.

Background The increase in DNA copy number in Down syndrome (DS; caused by trisomy 21) has led to the DNA dosage hypothesis, which posits that the level of gene expression is proportional to the gene’s DNA copy number. Yet many reports have suggested that a proportion of chromosome 21 genes are dosage compensated back towards typical expression levels (1.0×). In contrast, other reports suggest that dosage compensation is not a common mechanism of gene regulation in trisomy 21, providing support to the DNA dosage hypothesis. Results In our work, we use both simulated and real data to dissect the elements of differential expression analysis that can lead to the appearance of dosage compensation, even when compensation is demonstrably absent. Using lymphoblastoid cell lines derived from a family with an individual with Down syndrome, we demonstrate that dosage compensation is nearly absent at both nascent transcription (GRO-seq) and steady-state RNA (RNA-seq) levels. Furthermore, we link the limited apparent dosage compensation to expected allelic variation in transcription levels. Conclusions Transcription dosage compensation does not occur in Down syndrome. Simulated data containing no dosage compensation can appear to have dosage compensation when analyzed via standard methods. Moreover, some chromosome 21 genes that appear to be dosage compensated are consistent with allele specific expression.

Background A variety of protocols exist for producing whole genome run-on transcription datasets. However, little is known about how differences between these protocols affect the signal within the resulting libraries. Results Using run-on transcription datasets generated from the same biological system, we show that a variety of GRO- and PRO-seq preparation methods leave identifiable signatures within each library. Specifically we show that the library preparation method results in differences in quality control metrics, as well as differences in the signal distribution at the 5 ′ end of transcribed regions. These shifts lead to disparities in eRNA identification, but do not impact analyses aimed at inferring the key regulators involved in changes to transcription. Conclusions Run-on sequencing protocol variations result in technical signatures that can be used to identify both the enrichment and library preparation method of a particular data set. These technical signatures are batch effects that limit detailed comparisons of pausing ratios and eRNAs identified across protocols. However, these batch effects have only limited impact on our ability to infer which regulators underlie the observed transcriptional changes.

Background Global run-on coupled with deep sequencing (GRO-seq) provides extensive information on the location and function of coding and non-coding transcripts, including primary microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and enhancer RNAs (eRNAs), as well as yet undiscovered classes of transcripts. However, few computational tools tailored toward this new type of sequencing data are available, limiting the applicability of GRO-seq data for identifying novel transcription units. Results Here, we present groHMM, a computational tool in R, which defines the boundaries of transcription units de novo using a two state hidden-Markov model (HMM). A systematic comparison of the performance between groHMM and two existing peak-calling methods tuned to identify broad regions (SICER and HOMER) favorably supports our approach on existing GRO-seq data from MCF-7 breast cancer cells. To demonstrate the broader utility of our approach, we have used groHMM to annotate a diverse array of transcription units ( i.e. , primary transcripts) from four GRO-seq data sets derived from cells representing a variety of different human tissue types, including non-transformed cells (cardiomyocytes and lung fibroblasts) and transformed cells (LNCaP and MCF-7 cancer cells), as well as non-mammalian cells (from flies and worms). As an example of the utility of groHMM and its application to questions about the transcriptome, we show how groHMM can be used to analyze cell type-specific enhancers as defined by newly annotated enhancer transcripts. Conclusions Our results show that groHMM can reveal new insights into cell type-specific transcription by identifying novel transcription units, and serve as a complete and useful tool for evaluating functional genomic elements in cells.

Control of gene expression is fundamental at every level of cell function. Promoter-proximal pausing and divergent transcription at promoters and enhancers, which are prominent features in animals, have only been studied in a handful of research experiments in plants. PRO-Seq analysis in cassava (Manihot esculenta) identified peaks of transcriptionally engaged RNA polymerase at both the 5′ and 3′ end of genes, consistent with paused or slowly moving Polymerase. In addition, we identified divergent transcription at intergenic sites. A full genome search for bi-directional transcription using an algorithm for enhancer detection developed in mammals (dREG) identified many intergenic regulatory element (IRE) candidates. These sites showed distinct patterns of methylation and nucleotide conservation based on genomic evolutionary rate profiling (GERP). SNPs within these IRE candidates explained significantly more variation in fitness and root composition than SNPs in chromosomal segments randomly ascertained from the same intergenic distribution, strongly suggesting a functional importance of these sites. Maize GRO-Seq data showed RNA polymerase occupancy at IREs consistent with patterns in cassava. Furthermore, these IREs in maize significantly overlapped with sites previously identified on the basis of open chromatin, histone marks, and methylation, and were enriched for reported eQTL. Our results suggest that bidirectional transcription can identify intergenic genomic regions in plants that play an important role in transcription regulation and whose identification has the potential to aid crop improvement.

Treatment with histone deacetylase inhibitors (HDACI) results in potent cytotoxicity of a variety of cancer cell types, and these drugs are used clinically to treat hematological tumors. They are known to repress the transcription of ERBB2 and many other oncogenes, but little is known about this mechanism. Using global run-on sequencing (GRO-seq) to measure nascent transcription, we find that HDACI cause transcriptional repression by blocking RNA polymerase II elongation. Our data show that HDACI preferentially repress the transcription of highly expressed genes as well as high copy number genes in HER2+ breast cancer genomes. In contrast, genes that are activated by HDACI are moderately expressed. We analyzed gene copy number in combination with microarray and GRO-seq analysis of expression level, in normal and breast cancer cells to show that high copy number genes are more likely to be repressed by HDACI than non-amplified genes. The inhibition of transcription of amplified oncogenes, which promote survival and proliferation of cancer cells, might explain the cancer-specific lethality of HDACI, and may represent a general therapeutic strategy for cancer.