Transcription dosage compensation does not occur in Down syndrome

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.

Mapping Transcription Regulation with Run-on and Sequencing Data Using the Web-Based tfTarget Gateway

Run-on and sequencing assays like GRO-seq, PRO-seq, and ChRO-seq allow for joint profiling of transcription activity of transcriptional regulatory elements (TREs), i.e., promoters and active enhancers, and target genes. Variation in biological conditions, such as treated vs. control, results in changes in the activity of transcription factors (TFs), which induces concerted changes in TREs and target genes. By modeling the differences between two biological conditions, we developed the computational pipeline known as tfTarget that predicts a set of putative TREs and target genes responding to each TF under the biological condition of interest. In this chapter, we demonstrate the use of the new web-based tfTarget in mapping transcription regulation using run-on sequencing data.

Protocol variations in run-on transcription dataset preparation produce detectable signatures in sequencing libraries

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.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12864-022-08352-8).

RNA polymerase mapping in plants identifies intergenic regulatory elements enriched in causal variants

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.

Super-Enhancer Redistribution as a Mechanism of Broad Gene Dysregulation in Repeatedly Drug-Treated Cancer Cells

Cisplatin is an antineoplastic drug administered at suboptimal and intermittent doses to avoid life-threatening effects. Although this regimen shortly improves symptoms in the short term, it also leads to more malignant disease in the long term. We describe a multilayered analysis ranging from chromatin to translation-integrating chromatin immunoprecipitation sequencing (ChIP-seq), global run-on sequencing (GRO-seq), RNA sequencing (RNA-seq), and ribosome profiling-to understand how cisplatin confers (pre)malignant features by using a well-established ovarian cancer model of cisplatin exposure. This approach allows us to segregate the human transcriptome into gene modules representing distinct regulatory principles and to characterize that the most cisplatin-disrupted modules are associated with underlying events of super-enhancer plasticity. These events arise when cancer cells initiate without ultimately ending the program of drug-stimulated death. Using a PageRank-based algorithm, we predict super-enhancer regulator ISL1 as a driver of this plasticity and validate this prediction by using CRISPR/dCas9-KRAB inhibition (CRISPRi) and CRISPR/dCas9-VP64 activation (CRISPRa) tools. Together, we propose that cisplatin reprograms cancer cells when inducing them to undergo near-to-death experiences.

Global Analyses to Identify Direct Transcriptional Targets of p53

The transcription factor p53 controls a gene expression program with pleiotropic effects on cell biology including cell cycle arrest and apoptosis. Identifying direct p53 target genes within this network and determining how they influence cell fate decisions downstream of p53 activation is a prerequisite for designing therapeutic approaches that target p53 to effectively kill cancer cells. Here we describe a comprehensive multi-omics approach for identifying genes that are direct transcriptional targets of p53. We provide detailed procedures for measuring global RNA polymerase activity, defining p53 binding sites across the genome, and quantifying changes in steady-state mRNA in response to p53 activation.

An Enhancer-Based Analysis Revealed a New Function of Androgen Receptor in Tumor Cell Immune Evasion

Cancer is characterized by dysregulation at multiple levels, such as gene transcription. Enhancers are well-studied transcription regulators that can enhance target transcripts through DNA loop formation mediated by chromosome folding. The gain or loss of the interaction between an enhancer and its target gene has a critical effect on gene expression. In this study, we analyzed GRO-seq data to identify active enhancers from seven common cancer cell lines and studied the function of these enhancers across multiple cancer types. By constructing an "enhancer effect score" (EES), we found a significant correlation between EES and tumor-infiltrating lymphocytes (TILs) in prostate cancer. Further analysis revealed that androgen receptor (AR) plays an important role in regulating the immune checkpoint gene PVR via its enhancer. These results suggest that AR contributes to prostate cancer aggressiveness by promoting cancer cell immune evasion.

Analysis of H3K4me3 and H3K27me3 bivalent promotors in HER2+ breast cancer cell lines reveals variations depending on estrogen receptor status and significantly correlates with gene expression

BACKGROUND

The role of histone modifications is poorly characterized in breast cancer, especially within the major subtypes. While epigenetic modifications may enhance the adaptability of a cell to both therapy and the surrounding environment, the mechanisms by which this is accomplished remains unclear. In this study we focus on the HER2 subtype and investigate two histone trimethylations that occur on the histone 3; the trimethylation located at lysine 4 (H3K4me3) found in active promoters and the trimethylation located at lysine 27 (H3K27me3) that correlates with gene repression. A bivalency state is the result of the co-presence of these two marks at the same promoter.

METHODS

In this study we investigated the relationship between these histone modifications in promoter regions and their proximal gene expression in HER2+ breast cancer cell lines. In addition, we assessed these patterns with respect to the presence or absence of the estrogen receptor (ER). To do this, we utilized ChIP-seq and matching RNA-seq from publicly available data for the AU565, SKBR3, MB361 and UACC812 cell lines. In order to visualize these relationships, we used KEGG pathway enrichment analysis, and Kaplan-Meyer plots.

RESULTS

We found that the correlation between the three types of promoter trimethylation statuses (H3K4me3, H3K27me3 or both) and the expression of the proximal genes was highly significant overall, while roughly a third of all genes are regulated by this phenomenon. We also show that there are several pathways related to cancer progression and invasion that are associated with the bivalent status of the gene promoters, and that there are specific differences between ER+ and ER- HER2+ breast cancer cell lines. These specific differences that are differentially trimethylated are also shown to be differentially expressed in patient samples. One of these genes, HIF1AN, significantly correlates with patient outcome.

CONCLUSIONS

This study highlights the importance of looking at epigenetic markings at a subtype specific level by characterizing the relationship between the bivalent promoters and gene expression. This provides a deeper insight into a mechanism that could lead to future targets for treatment and prognosis, along with oncogenesis and response to therapy of HER2+ breast cancer patients.

Next Generation Sequencing (NGS) Application in Multiparameter Gene Expression Analysis

Next generation sequencing (NGS) is routinely used in gene expression analyses. In particular, RNA-seq has been the method of choice for highly sensitive genome-wide quantification of RNA expression. The method can be used in a wide variety of model systems, including studies to gain insight into underlying mechanisms of toxicologic processes and disease development induced by environmental toxicants. RNA-seq has also been coupled to many other molecular biology protocols to monitor specific aspects of the gene expression process. Here, we describe two such coupling-(a) global run-on sequencing (GRO-seq) that coupled it to the nuclear run-on (NRO), and (b) polysome profiling that coupled it to sucrose-gradient-based polysome isolation. Simultaneous RNA-seq, GRO-seq, and polysome profiling analyses enabled genome-wide analysis of the mode of stability control of individual RNA molecules.

TDP-43 regulates transcription at protein-coding genes and Alu retrotransposons

The 43-kDa transactive response DNA-binding protein (TDP-43) is an example of an RNA-binding protein that regulates RNA metabolism at multiple levels from transcription and splicing to translation. Its role in post-transcriptional RNA processing has been a primary focus of recent research, but its role in regulating transcription has been studied for only a few human genes. We characterized the effects of TDP-43 on transcription genome-wide and found that TDP-43 broadly affects transcription of protein-coding and noncoding RNA genes. Among protein-coding genes, the effects of TDP-43 were greatest for genes <30 thousand base pairs in length. Surprisingly, we found that the loss of TDP-43 resulted in increased evidence for transcription activity near repetitive Alu elements found within expressed genes. The highest densities of affected Alu elements were found in the shorter genes, whose transcription was most affected by TDP-43. Thus, in addition to its role in post-transcriptional RNA processing, TDP-43 plays a critical role in maintaining the transcriptional stability of protein-coding genes and transposable DNA elements.

Discovering Transcriptional Regulatory Elements From Run-On and Sequencing Data Using the Web-Based dREG Gateway

Transcription is a chromatin mark that can be used effectively to identify the location of active enhancers and promoters, collectively known as transcriptional regulatory elements (TREs). We recently introduced dREG, a tool for the identification of TREs using run-on and sequencing (RO-seq) assays, including global run-on and sequencing (GRO-seq), precision run-on and sequencing (PRO-seq), and chromatin run-on and sequencing (ChRO-seq). In this protocol, we present step-by-step instructions for running dREG on an arbitrary run-on and sequencing dataset. Users provide dREG with bigWig files (in which each read is represented by a single base) representing the location of RNA polymerase in a cell or tissue sample of interest, and dREG returns a list of genomic regions that are predicted to be active TREs. Finally, we demonstrate the use of dREG regions in discovering transcription factors controlling response to a stimulus and predicting their target genes. Together, this protocol provides detailed instructions for running dREG on arbitrary run-on and sequencing data. © 2018 by John Wiley & Sons, Inc.

Measuring RNA polymerase activity genome-wide with high-resolution run-on-based methods

The biogenesis of RNAs is a multi-layered and highly regulated process that involves a diverse set of players acting in an orchestrated manner throughout the transcription cycle. Transcription initiation, elongation and termination factors act on RNA polymerases to modulate their movement along the DNA template in a very precise manner, more complex than previously anticipated. Genome-scale run-on-based methodologies have been developed to study in detail the position of transcriptionally-engaged RNA polymerases. Genomic run-on (GRO), and its many variants and refinements made over the years, are helping the community to address an increasing amount of scientific questions, spanning an increasing range of organisms and systems. In this review, we aim to summarize the most relevant high throughput methodologies developed to study nascent RNA by run-on methods, compare their main features, advantages and limitations, while putting them in context with alternative ways of studying the transcriptional process.

Identification and removal of sequencing artifacts produced by mispriming during reverse transcription in multiple RNA-seq technologies

The quality of RNA sequencing data relies on specific priming by the primer used for reverse transcription (RT-primer). Nonspecific annealing of the RT-primer to the RNA template can generate reads with incorrect cDNA ends and can cause misinterpretation of data (RT mispriming). This kind of artifact in RNA-seq based technologies is underappreciated and currently no adequate tools exist to computationally remove them from published data sets. We show that mispriming can occur with as little as two bases of complementarity at the 3' end of the primer followed by intermittent regions of complementarity. We also provide a computational pipeline that identifies cDNA reads produced from RT mispriming, allowing users to filter them out from any aligned data set. Using this analysis pipeline, we identify thousands of mispriming events in a dozen published data sets from diverse technologies including short RNA-seq, total/mRNA-seq, HITS-CLIP, and GRO-seq. We further show how RT mispriming can lead to misinterpretation of data. In addition to providing a solution to computationally remove RT-misprimed reads, we also propose an experimental solution to completely avoid RT-mispriming by performing RNA-seq using thermostable group II intron derived reverse transcriptase (TGIRT-seq).

FOCS: a novel method for analyzing enhancer and gene activity patterns infers an extensive enhancer-promoter map

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.

CIPHER: a flexible and extensive workflow platform for integrative next-generation sequencing data analysis and genomic regulatory element prediction

BACKGROUND

Next-generation sequencing (NGS) approaches are commonly used to identify key regulatory networks that drive transcriptional programs. Although these technologies are frequently used in biological studies, NGS data analysis remains a challenging, time-consuming, and often irreproducible process. Therefore, there is a need for a comprehensive and flexible workflow platform that can accelerate data processing and analysis so more time can be spent on functional studies.

RESULTS

We have developed an integrative, stand-alone workflow platform, named CIPHER, for the systematic analysis of several commonly used NGS datasets including ChIP-seq, RNA-seq, MNase-seq, DNase-seq, GRO-seq, and ATAC-seq data. CIPHER implements various open source software packages, in-house scripts, and Docker containers to analyze and process single-ended and pair-ended datasets. CIPHER's pipelines conduct extensive quality and contamination control checks, as well as comprehensive downstream analysis. A typical CIPHER workflow includes: (1) raw sequence evaluation, (2) read trimming and adapter removal, (3) read mapping and quality filtering, (4) visualization track generation, and (5) extensive quality control assessment. Furthermore, CIPHER conducts downstream analysis such as: narrow and broad peak calling, peak annotation, and motif identification for ChIP-seq, differential gene expression analysis for RNA-seq, nucleosome positioning for MNase-seq, DNase hypersensitive site mapping, site annotation and motif identification for DNase-seq, analysis of nascent transcription from Global-Run On (GRO-seq) data, and characterization of chromatin accessibility from ATAC-seq datasets. In addition, CIPHER contains an "analysis" mode that completes complex bioinformatics tasks such as enhancer discovery and provides functions to integrate various datasets together.

CONCLUSIONS

Using public and simulated data, we demonstrate that CIPHER is an efficient and comprehensive workflow platform that can analyze several NGS datasets commonly used in genome biology studies. Additionally, CIPHER's integrative "analysis" mode allows researchers to elicit important biological information from the combined dataset analysis.

Deregulation of the non-coding genome in leukemia

Methodological advances that allow deeper characterization of non-coding elements in the genome have started to reveal the full spectrum of deregulation in cancer. We generated an inducible cell model to track transcriptional changes after induction of a well-known leukemia-inducing fusion gene, ETV6-RUNX1. Our data revealed widespread transcriptional alterations outside coding elements in the genome. This adds to the growing list of various alterations in the non-coding genome in cancer and pinpoints their role in diseased cellular state.

Transcriptional landscape of the human cell cycle

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.

Computational Approaches for Mining GRO-Seq Data to Identify and Characterize Active Enhancers

Transcriptional enhancers are DNA regulatory elements that are bound by transcription factors and act to positively regulate the expression of nearby or distally located target genes. Enhancers have many features that have been discovered using genomic analyses. Recent studies have shown that active enhancers recruit RNA polymerase II (Pol II) and are transcribed, producing enhancer RNAs (eRNAs). GRO-seq, a method for identifying the location and orientation of all actively transcribing RNA polymerases across the genome, is a powerful approach for monitoring nascent enhancer transcription. Furthermore, the unique pattern of enhancer transcription can be used to identify enhancers in the absence of any information about the underlying transcription factors. Here, we describe the computational approaches required to identify and analyze active enhancers using GRO-seq data, including data pre-processing, alignment, and transcript calling. In addition, we describe protocols and computational pipelines for mining GRO-seq data to identify active enhancers, as well as known transcription factor binding sites that are transcribed. Furthermore, we discuss approaches for integrating GRO-seq-based enhancer data with other genomic data, including target gene expression and function. Finally, we describe molecular biology assays that can be used to confirm and explore further the function of enhancers that have been identified using genomic assays. Together, these approaches should allow the user to identify and explore the features and biological functions of new cell type-specific enhancers.

GRO-seq, A Tool for Identification of Transcripts Regulating Gene Expression

The advent of next-generation sequencing (NGS) technologies has revolutionized the way we do research on gene expression. High-throughput transcriptomics became possible with the development of microarray technology, but its widespread application only occurred after the emergence of massive parallel sequencing. Especially, RNA sequencing (RNA-seq) has greatly increased our knowledge about the genome and led to the identification and annotation of novel classes of RNAs in different species. However, RNA-seq measures the steady-state level of a given RNA, which is the equilibrium between transcription, processing, and degradation. In recent years, a number of dedicated RNA-seq technologies were developed to measure specifically transcription events. Global run-on sequencing (GRO-seq) is the most widely used method to measure nascent RNA, and in recent years, it has been applied successfully to study the function and mechanism of action of noncoding RNAs. Here, we describe a detailed protocol of GRO-seq that can be readily applied to investigate different aspects of RNA biology in human cells.

Nascent RNA sequencing reveals distinct features in plant transcription

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.

Transcription-coupled genetic instability marks acute lymphoblastic leukemia structural variation hotspots

Progression of malignancy to overt disease requires multiple genetic hits. Activation-induced deaminase (AID) can drive lymphomagenesis by generating off-target DNA breaks at loci that harbor highly active enhancers and display convergent transcription. The first active transcriptional profiles from acute lymphoblastic leukemia (ALL) patients acquired here reveal striking similarity at structural variation (SV) sites. Specific transcriptional features, namely convergent transcription and Pol2 stalling, were detected at breakpoints. The overlap was most prominent at SV with recognition motifs for the recombination activating genes (RAG). We present signal feature analysis to detect vulnerable regions and quantified from human cells how convergent transcription contributes to R-loop generation and RNA polymerase stalling. Wide stalling regions were characterized by high DNAse hypersensitivity and unusually broad H3K4me3 signal. Based on 1382 pre-B-ALL patients, the ETV6-RUNX1 fusion positive patients had over ten-fold elevation in RAG1 while high expression of AID marked pre-B-ALL lacking common cytogenetic changes.

RNA Pol II transcription model and interpretation of GRO-seq data

A mixture model and statistical method is proposed to interpret the distribution of reads from a nascent transcriptional assay, such as global run-on sequencing (GRO-seq) data. The model is annotation agnostic and leverages on current understanding of the behavior of RNA polymerase II. Briefly, it assumes that polymerase loads at key positions (transcription start sites) within the genome. Once loaded, polymerase either remains in the initiation form (with some probability) or transitions into an elongating form (with the remaining probability). The model can be fit genome-wide, allowing patterns of Pol II behavior to be assessed on each distinct transcript. Furthermore, it allows for the first time a principled approach to distinguishing the initiation signal from the elongation signal; in particular, it implies a data driven method for calculating the pausing index, a commonly used metric that informs on the behavior of RNA polymerase II. We demonstrate that this approach improves on existing analyses of GRO-seq data and uncovers a novel biological understanding of the impact of knocking down the Male Specific Lethal (MSL) complex in Drosophilia melanogaster.

Myosin VI regulates gene pairing and transcriptional pause release in T cells

Naive CD4 T cells differentiate into several effector lineages, which generate a stronger and more rapid response to previously encountered immunological challenges. Although effector function is a key feature of adaptive immunity, the molecular basis of this process is poorly understood. Here, we investigated the spatiotemporal regulation of cytokine gene expression in resting and restimulated effector T helper 1 (Th1) cells. We found that the Lymphotoxin (LT)/TNF alleles, which encode TNF-α, were closely juxtaposed shortly after T-cell receptor (TCR) engagement, when transcription factors are limiting. Allelic pairing required a nuclear myosin, myosin VI, which is rapidly recruited to the LT/TNF locus upon restimulation. Furthermore, transcription was paused at the TNF locus and other related genes in resting Th1 cells and released in a myosin VI-dependent manner following activation. We propose that homologous pairing and myosin VI-mediated transcriptional pause release account for the rapid and efficient expression of genes induced by an external stimulus.

Genome-wide dynamics of Pol II elongation and its interplay with promoter proximal pausing, chromatin, and exons

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.DOI: http://dx.doi.org/10.7554/eLife.02407.001.

Hormone-regulated transcriptomes: lessons learned from estrogen signaling pathways in breast cancer cells

Recent rapid advances in next generation sequencing technologies have expanded our understanding of steroid hormone signaling to a genome-wide level. In this review, we discuss the use of a novel genomic approach, global nuclear run-on coupled with massively parallel sequencing (GRO-seq), to explore new facets of the steroid hormone-regulated transcriptome, especially estrogen responses in breast cancer cells. GRO-seq is a high throughput sequencing method adapted from conventional nuclear run-on methodologies, which is used to obtain a map of the position and orientation of all transcriptionally engaged RNA polymerases across the genome with extremely high spatial resolution. GRO-seq, which is an excellent tool for examining transcriptional responses to extracellular stimuli, has been used to comprehensively assay the effects of estrogen signaling on the transcriptome of ERα-positive MCF-7 human breast cancer cells. These studies have revealed new details about estrogen-dependent transcriptional regulation, including effects on transcription by all three RNA polymerases, complex transcriptional dynamics in response to estrogen signaling, and identification novel, unannotated non-coding RNAs. Collectively, these studies have been useful in discerning the molecular logic of the estrogen-regulated mitogenic response.

Extensive polymerase pausing during Drosophila axis patterning enables high-level and pliable transcription

Cascades of zygotic gene expression pattern the anterior-posterior (AP) and dorsal-ventral (DV) axes of the early Drosophila embryo. Here, we used the global run-on sequencing assay (GRO-seq) to map the genome-wide RNA polymerase distribution during early Drosophila embryogenesis, thus providing insights into how genes are regulated. We identify widespread promoter-proximal pausing yet show that the presence of paused polymerase does not necessarily equate to direct regulation through pause release to productive elongation. Our data reveal that a subset of early Zelda-activated genes is regulated at the level of polymerase recruitment, whereas other Zelda target and axis patterning genes are predominantly regulated through pause release. In contrast to other signaling pathways, we found that bone morphogenetic protein (BMP) target genes are collectively more highly paused than BMP pathway components and show that BMP target gene expression requires the pause-inducing negative elongation factor (NELF) complex. Our data also suggest that polymerase pausing allows plasticity in gene activation throughout embryogenesis, as transiently repressed and transcriptionally silenced genes maintain and lose promoter polymerases, respectively. Finally, we provide evidence that the major effect of pausing is on the levels, rather than timing, of transcription. These data are discussed in terms of the efficiency of transcriptional activation required across cell populations during developmental time constraints.