Search

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We present a fast and simple algorithm to detect nascent RNA transcription in global nuclear run-on sequencing (GRO-seq). GRO-seq is a relatively new protocol that captures nascent transcripts from actively engaged polymerase, providing a direct read-out on bona fide transcription. Most traditional assays, such as RNA-seq, measure steady state RNA levels which are affected by transcription, post-transcriptional processing, and RNA stability. GRO-seq data, however, presents unique analysis challenges that are only beginning to be addressed. Here, we describe a new algorithm, Fast Read Stitcher (FStitch), that takes advantage of two popular machine-learning techniques, hidden Markov models and logistic regression, to classify which regions of the genome are transcribed. Given a small user-defined training set, our algorithm is accurate, robust to varying read depth, annotation agnostic, and fast. Analysis of GRO-seq data without a priori need for annotation uncovers surprising new insights into several aspects of the transcription process. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

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 histonemodification 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 earlyMphase 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 steadystate 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. • A 6-layered multiomic approach provides an integrated view of cisplatin treatment • Repeated cisplatin treatment leads to selective super-enhancer (SE) redistribution • The most cisplatin-dysregulated genes locate nearby redistributed SE regions • ISL1 is a SE regulator suppressed when cells struggle with cisplatin treatment Ovarian tumors acquire new phenotypic features when exposed to anticancer therapy, which helps them endure the struggle for survival and increase malignancy. Using a six-layered multiomic approach, Ma et al. find that selective super-enhancer redistribution is a major driver of the acquisition of new phenotypic features. [ABSTRACT FROM AUTHOR]