Global Run-On sequencing to measure nascent transcription in C. elegans.
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ABSTRACT:
SUBMITTER: Quarato P
PROVIDER: S-EPMC8649397 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Transcription is the primary regulatory step in gene expression. Divergent transcription initiation from promoters and enhancers produces stable RNAs from genes and unstable RNAs from enhancers1,2. Nascent RNA capture and sequencing assays simultaneously measure gene and enhancer activity in cell populations3. However, fundamental questions about the temporal regulation of transcription and enhancer-gene coordination remain unanswered, primarily because of the absence of a single-cell perspective on active transcription. In this study, we present scGRO-seq-a new single-cell nascent RNA sequencing assay that uses click chemistry-and unveil coordinated transcription throughout the genome. We demonstrate the episodic nature of transcription and the co-transcription of functionally related genes. scGRO-seq can estimate burst size and frequency by directly quantifying transcribing RNA polymerases in individual cells and can leverage replication-dependent non-polyadenylated histone gene transcription to elucidate cell cycle dynamics. The single-nucleotide spatial and temporal resolution of scGRO-seq enables the identification of networks of enhancers and genes. Our results suggest that the bursting of transcription at super-enhancers precedes bursting from associated genes. By imparting insights into the dynamic nature of global transcription and the origin and propagation of transcription signals, we demonstrate the ability of scGRO-seq to investigate the mechanisms of transcription regulation and the role of enhancers in gene expression.
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Project description:In experiments with significant perturbations to transcription, nascent RNA sequencing protocols are dependent on external spike-ins for reliable normalization. Unlike in RNA-seq, these spike-ins are not standardized and, in many cases, depend on a run-on reaction that is assumed to have constant efficiency across samples. To assess the validity of this assumption, we analyze a large number of published nascent RNA spike-ins to quantify their variability across existing normalization methods. Furthermore, we develop a new biologically-informed Bayesian model to estimate the error in spike-in based normalization estimates, which we term Virtual Spike-In (VSI). We apply this method both to published external spike-ins as well as using reads at the [Formula: see text] end of long genes, building on prior work from Mahat (Mol Cell 62(1):63-78, 2016. https://doi.org/10.1016/j.molcel.2016.02.025 ) and Vihervaara (Nat Commun 8(1):255, 2017. https://doi.org/10.1038/s41467-017-00151-0 ). We find that spike-ins in existing nascent RNA experiments are typically under sequenced, with high variability between samples. Furthermore, we show that these high variability estimates can have significant downstream effects on analysis, complicating biological interpretations of results.
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Project description:Nuclear run-on (NRO) is a method that measures transcriptional activity via the quantification of biochemically labeled nascent RNA molecules derived from nuclear isolates. Widespread use of this technique has been limited because of its technical difficulty relative to steady-state total mRNA analyses. Here we describe a detailed protocol for the quantification of transcriptional activity in human cell cultures. Nuclei are first isolated and NRO transcription is performed in the presence of bromouridine. Labeled nascent transcripts are purified by immunoprecipitation, and transcript levels are determined by reverse-transcription quantitative PCR (RT-qPCR). Data are then analyzed using standard techniques described elsewhere. This method is rapid (the protocol can be completed in 2 d) and cost-effective, exhibits negligible detection of background noise from unlabeled transcripts, requires no radioactive materials and can be performed from as few as 500,000 nuclei. It also takes advantage of the high sensitivity, specificity and dynamic range of RT-qPCR.
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Project description:Recent studies of transcription have revealed a level of complexity not previously appreciated even a few years ago, both in the intricate use of post-initiation control and the mass production of rapidly degraded transcripts. Dissection of these pathways requires strategies for precisely following transcripts as they are being produced. Here we present an approach (native elongating transcript sequencing, NET-seq), based on deep sequencing of 3' ends of nascent transcripts associated with RNA polymerase, to monitor transcription at nucleotide resolution. Application of NET-seq in Saccharomyces cerevisiae reveals that although promoters are generally capable of divergent transcription, the Rpd3S deacetylation complex enforces strong directionality to most promoters by suppressing antisense transcript initiation. Our studies also reveal pervasive polymerase pausing and backtracking throughout the body of transcripts. Average pause density shows prominent peaks at each of the first four nucleosomes, with the peak location occurring in good agreement with in vitro biophysical measurements. Thus, nucleosome-induced pausing represents a major barrier to transcriptional elongation in vivo.
| S-EPMC3880149 | biostudies-literature
Project description:Antisense transcription can regulate sense gene expression. However, previous annotations of antisense transcription units have been based on detection of mature antisense long noncoding (aslnc)RNAs by RNA-seq and/or microarrays, only giving a partial view of the antisense transcription landscape and incomplete molecular bases for antisense-mediated regulation. Here, we used native elongating transcript sequencing to map genome-wide nascent antisense transcription in fission yeast. Strikingly, antisense transcription was detected for most protein-coding genes, correlating with low sense transcription, especially when overlapping the mRNA start site. RNA profiling revealed that the resulting aslncRNAs mainly correspond to cryptic Xrn1/Exo2-sensitive transcripts (XUTs). ChIP-seq analyses showed that antisense (as)XUT's expression is associated with specific histone modification patterns. Finally, we showed that asXUTs are controlled by the histone chaperone Spt6 and respond to meiosis induction, in both cases anti-correlating with levels of the paired-sense mRNAs, supporting physiological significance to antisense-mediated gene attenuation. Our work highlights that antisense transcription is much more extended than anticipated and might constitute an additional nonpromoter determinant of gene regulation complexity.
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Project description: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.
| S-EPMC5087027 | biostudies-literature
Project description: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.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.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.
| S-EPMC4502638 | biostudies-literature
Project description:Researchers may be interested in finding proteomics runs, which have been deposited into online repositories, that are similar to their own data. However, it is difficult to measure the similarity of a pair of proteomics runs. Here, we present a new method, MS1Connect, that only uses intact peptide scans to calculate the similarity between a pair of runs. We show evidence that the MS1Connect score accurately measures the similarity between two proteomics runs. Specifically, we show that MS1Connect outperforms baseline methods for predicting the species a sample originated. In addition, we show that MS1Connect scores are highly correlated with similarities based o peptide fragment scans by observing a high correlation between MS1Connect scores and the Jaccard index between the sets of confidently detected peptides for a pair of runs.
2023-01-05 | PXD027791 | Pride
Project description:MotivationInterpretation of newly acquired mass spectrometry data can be improved by identifying, from an online repository, previous mass spectrometry runs that resemble the new data. However, this retrieval task requires computing the similarity between an arbitrary pair of mass spectrometry runs. This is particularly challenging for runs acquired using different experimental protocols.ResultsWe propose a method, MS1Connect, that calculates the similarity between a pair of runs by examining only the intact peptide (MS1) scans, and we show evidence that the MS1Connect score is accurate. Specifically, we show that MS1Connect outperforms several baseline methods on the task of predicting the species from which a given proteomics sample originated. In addition, we show that MS1Connect scores are highly correlated with similarities computed from fragment (MS2) scans, even though these data are not used by MS1Connect.Availability and implementationThe MS1Connect software is available at https://github.com/bmx8177/MS1Connect.Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:The goal of genomics and systems biology is to understand how complex systems of factors assemble into pathways and structures that combine to form living organisms. Great advances in understanding biological processes result from determining the function of individual genes, a process that has classically relied on characterizing single mutations. Advances in DNA sequencing has made available the complete set of genetic instructions for an astonishing and growing number of species. To understand the function of this ever-increasing number of genes, a high-throughput method was developed that in a single experiment can measure the function of genes across the genome of an organism. This occurred approximately 10 years ago, when high-throughput DNA sequencing was combined with advances in transposon-mediated mutagenesis in a method termed transposon insertion sequencing (TIS). In the subsequent years, TIS succeeded in addressing fundamental questions regarding the genes of bacteria, many of which have been shown to play central roles in bacterial infections that result in major human diseases. The field of TIS has matured and resulted in studies of hundreds of species that include significant innovations with a number of transposons. Here, we summarize a number of TIS experiments to provide an understanding of the method and explanation of approaches that are instructive when designing a study. Importantly, we emphasize critical aspects of a TIS experiment and highlight the extension and applicability of TIS into nonbacterial species such as yeast.
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