Hammerhead-type FXR agonists induce an eRNA FincoR that ameliorates nonalcoholic steatohepatitis in mice.

The nuclear receptor, Farnesoid X Receptor (FXR/NR1H4), is increasingly recognized as a promising drug target for metabolic diseases, including nonalcoholic steatohepatitis (NASH). Protein coding genes regulated by FXR are well known, but whether FXR also acts through regulation of long non-coding RNAs (lncRNAs), which vastly outnumber protein-coding genes, remains unknown. Utilizing RNA-seq and GRO-seq analyses in mouse liver, we found that FXR activation affects the expression of many RNA transcripts from chromatin regions bearing enhancer features. Among these we discovered a previously unannotated liver-enriched enhancer-derived lncRNA (eRNA), termed FincoR. We show that FincoR is specifically induced by the hammerhead-type FXR agonists, including GW4064 and tropifexor. CRISPR/Cas9-mediated liver-specific knockdown of FincoR in dietary NASH mice reduced the beneficial effects of tropifexor, an FXR agonist currently in clinical trials for NASH and primary biliary cholangitis (PBC), indicating that that amelioration of liver fibrosis and inflammation in NASH treatment by tropifexor is mediated in part by FincoR. Overall, our findings highlight that pharmacological activation of FXR by hammerhead-type agonists induces a novel eRNA, FincoR, contributing to the amelioration of NASH in mice. FincoR may represent a new drug target for addressing metabolic disorders, including NASH.

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.

Nascent Transcript Sequencing for the Mapping of Promoters in Arabidopsis thaliana Mitochondria.

Knowledge of mitochondrial transcription start sites and promoter sequences is key to understanding mechanisms of transcription initiation in plant mitochondria. Transcription start sites can be straightforwardly determined by the mapping of primary transcript 5' ends. This chapter describes a next-generation sequencing-based protocol for the mitochondrial genome-wide mapping of transcription start sites in Arabidopsis thaliana. Like other strategies aiming at the determination of primary transcript 5' ends, this protocol exploits that only primary but not processed transcripts are 5'-triphosphorylated and, based on this property, can be enzymatically selected for. However, it uses nascent transcripts, in order to (1) enhance mitochondrial coverage compared with other compartments, (2) reduce rRNA and other background, and (3) also capture the primary 5' ends of rapidly degraded or processed transcripts.

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.

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.

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.

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.

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.

Development of a high efficient promoter finding method based on transient transfection.

In metazoan genome, the mechanism of gene expression regulation between transcriptional regulatory elements and their target gene is spatiotemporal. Active promoters possess many specific chromosomal features, such as hypersensitive to DNaseI and enrichment of specific histone modifications. In this article, we proposed a novel method which possesses a high efficiency to find promoters in vitro. A promoter-trap library was constructed with totally 706 random mouse genomic DNA fragment clones, and 260 promoter-active fragments of the library were screened by transient transfection into 4T1 cells. To demonstrate the accuracy of this promoter finding method, 13 fragments with promoter activities were randomly selected for published DNase-seq and ChIP-seq data analysis, downstream transcripts prediction and expression confirmation. qRT-PCR results showed that six predicted transcription units were successfully amplified in different mouse tissues/cells or in reconstituted mouse mammary tumors. Our results indicate that this promoter finding method can successfully detect the promoter-active fragments and their downstream transcripts.

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.

Transcriptional Profiling of Hypoxia-Regulated Non-coding RNAs in Human Primary Endothelial Cells.

Hypoxia occurs in human atherosclerotic lesions and has multiple adverse effects on endothelial cell metabolism. Recently, key roles of long non-coding RNAs (lncRNAs) in the development of atherosclerosis have begun to emerge. In this study, we investigate the lncRNA profiles of human umbilical vein endothelial cells subjected to hypoxia using global run-on sequencing (GRO-Seq). We demonstrate that hypoxia regulates the nascent transcription of ~1800 lncRNAs. Interestingly, we uncover evidence that promoter-associated lncRNAs are more likely to be induced by hypoxia compared to enhancer-associated lncRNAs, which exhibit an equal distribution of up- and downregulated transcripts. We also demonstrate that hypoxia leads to a significant induction in the activity of super-enhancers next to transcription factors and other genes implicated in angiogenesis, cell survival and adhesion, whereas super-enhancers near several negative regulators of angiogenesis were repressed. Despite the majority of lncRNAs exhibiting low detection in RNA-Seq, a subset of lncRNAs were expressed at comparable levels to mRNAs. Among these, MALAT1, HYMAI, LOC730101, KIAA1656, and LOC339803 were found differentially expressed in human atherosclerotic lesions, compared to normal vascular tissue, and may thus serve as potential biomarkers for lesion hypoxia.

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.

Transcription pausing regulates mouse embryonic stem cell differentiation.

The pluripotency of embryonic stem cells (ESCs) relies on appropriate responsiveness to developmental cues. Promoter-proximal pausing of RNA polymerase II (Pol II) has been suggested to play a role in keeping genes poised for future activation. To identify the role of Pol II pausing in regulating ESC pluripotency, we have generated mouse ESCs carrying a mutation in the pause-inducing factor SPT5. Genomic studies reveal genome-wide reduction of paused Pol II caused by mutant SPT5 and further identify a tight correlation between pausing-mediated transcription effect and local chromatin environment. Functionally, this pausing-deficient SPT5 disrupts ESC differentiation upon removal of self-renewal signals. Thus, our study uncovers an important role of Pol II pausing in regulating ESC differentiation and suggests a model that Pol II pausing coordinates with epigenetic modification to influence transcription during mESC differentiation.

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.