Chromatin measurements reveal contributions of synthesis and decay to steady‐state mRNA levels

Tippmann, Sylvia C.; Ivánek, Robert; Gaidatzis, Dimos; Schöler, Anne; Hoerner, Leslie; Nimwegen, Erik van; Stadler, Peter F.; Stadler, Michael; Schübeler, Dirk

doi:10.1038/msb.2012.23

Cited by 53 publications

(84 citation statements)

References 77 publications

(135 reference statements)

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“…These findings indicate that as a general rule, most differences in RNA levels between cell types are accounted for by transcription. This is in agreement with earlier studies showing that gene expression levels can be accurately predicted using only chromatin modifications 36,43 or in addition based on transcriptionfactor binding data 44,45 . For tissue-specific miRNAs, expression is anti-correlated with mRNA expression (corresponding to ∆exon) of the cognate target genes 46,47 .…”

Section: Application Of Eisa To Different Cell Lines and Tissuessupporting

confidence: 82%

“…We therefore compared ∆exon -∆intron to changes in log 2 mRNA half-lives 36 from ESCs to terminal neurons ( Fig. 3b) and found that they were significantly correlated ( Fig.…”

Section: Eisa Recovers the Role Of Transcription During Neurogenesismentioning

confidence: 99%

“…This test determines whether two correlation coefficients are significantly different from one another, given that two variables are compared to a third common variable. npg A n A ly s i s expression are transcriptionally driven 36 . When readdressing this finding by quantifying both exonic and intronic changes from ESCs and terminal neurons using total RNA 36,37 , we found very high agreement between exonic and intronic changes ( Fig.…”

Section: Eisa Recovers the Role Of Transcription During Neurogenesismentioning

confidence: 99%

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Analysis of intronic and exonic reads in RNA-seq data characterizes transcriptional and post-transcriptional regulation

et al. 2015

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A n A ly s i s RNA-seq experiments generate reads derived not only from mature RNA transcripts but also from pre-mRNA. Here we present a computational approach called exon-intron split analysis (EISA) that measures changes in mature RNA and pre-mRNA reads across different experimental conditions to quantify transcriptional and post-transcriptional regulation of gene expression. We apply EISA to 17 diverse data sets to show that most intronic reads arise from nuclear RNA and changes in intronic read counts accurately predict changes in transcriptional activity. Furthermore, changes in posttranscriptional regulation can be predicted from differences between exonic and intronic changes. EISA reveals both transcriptional and post-transcriptional contributions to expression changes, increasing the amount of information that can be gained from RNA-seq data sets.Cellular RNAs are regulated at multiple stages, including transcription, RNA maturation and degradation. Several analytic methods have been developed to measure these processes on a transcriptomewide scale. For example, global run-on sequencing (GRO-seq) 1 uses incorporation of a nucleotide analog to enrich for nascent RNA. In Nascent-seq 2,3 , newly transcribed RNAs are isolated by purification of their complex with proteins and the DNA template. Cellular fractionation techniques 4 have also been adapted to measure nascent transcripts, which are enriched in the nucleus. mRNA half-lives have been determined, for example, by blockage of transcription followed by transcriptional profiling 5 . RNA sequencing, the most widely used method for transcriptome analysis, has been applied in numerous studies to determine steady-state mRNA levels 6,7 and alternative splicing events 8 and to identify previously unknown transcripts and noncoding RNAs 9-11 . In general, these protocols aim to enrich for mature mRNA by selection of polyadenylated RNA or by depletion of ribosomal RNA.Many computational methods (reviewed in refs. 12,13) have been developed for the analysis of RNA-seq data, to enable spliced alignment 14,15 , transcript assembly 16,17 , transcript quantification 14,18 and differential expression analysis [19][20][21] . Although RNA-seq mostly generates reads that map to exons, it also captures less abundant intronic sequences 6 . However, their interpretation has remained controversial. Some have suggested that they originate from DNA contamination and can thus be used as a quality metric for RNA-seq data 22 (see also RNA-seq guidelines of the Roadmap Epigenomics Consortium, http://www.roadmapepigenomics.org/), whereas others have hypothesized that they stem from unknown exons or intronic enhancers 6,7 . In a study based on exon arrays, probes mapping to introns were used to investigate pre-mRNA dynamics 23 . Three recent studies based on RNA-seq provided evidence that intronic reads might correlate with transcriptional activity. In two of these, the read coverage along introns was related to nascent transcription in combination with co-transcriptional splicing e...

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Section: Application Of Eisa To Different Cell Lines and Tissuessupporting

confidence: 82%

“…We therefore compared ∆exon -∆intron to changes in log 2 mRNA half-lives 36 from ESCs to terminal neurons ( Fig. 3b) and found that they were significantly correlated ( Fig.…”

Section: Eisa Recovers the Role Of Transcription During Neurogenesismentioning

confidence: 99%

Section: Eisa Recovers the Role Of Transcription During Neurogenesismentioning

confidence: 99%

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Analysis of intronic and exonic reads in RNA-seq data characterizes transcriptional and post-transcriptional regulation

et al. 2015

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“…In various cell types, including DCs, ∼80% of the observed variance in mRNA levels is shaped by transcription rates rather than posttranscriptional regulation (52). Accordingly, our data strongly suggest that IPs shape the transcriptome mainly via regulation of transcription factors and not by affecting mRNA stability.…”

Section: Discussionmentioning

confidence: 66%

Immunoproteasomes Shape the Transcriptome and Regulate the Function of Dendritic Cells

Verteuil

Rouette

Hardy

et al. 2014

The Journal of Immunology

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By regulating protein degradation, constitutive proteasomes (CPs) control practically all cellular functions. In addition to CPs, vertebrates express immunoproteasomes (IPs). The major nonredundant role ascribed to IPs is their enhanced ability to generate antigenic peptides. We report that CPs and IPs differentially regulate the expression of >8000 transcripts in maturing mouse dendritic cells (DCs) via regulation of signaling pathways such as IFN regulatory factors, STATs, and NF-κB. IPs regulate the transcription of many mRNAs and maturation of a few of them. Moreover, even when engineered to present optimal amounts of antigenic peptide, IP-deficient DCs are inefficient for in vivo T cell priming. Our study shows that the role of IPs in DCs is not limited to Ag processing and reveals a major nonredundant role for IPs in transcription regulation. The dramatic effect of IPs on the transcriptional landscape could explain the various immune and nonimmune phenotypes observed in vertebrates with IP deficiency or mutations.

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“…Thus, outside of the native genomic locus, this sequence recruits the Polycomb machinery in a cell-type-independent manner. The Utf1 locus codes for an established pluripotency factor (29) and is exclusively expressed in ES cells (30). Upon differentiation into neuronal progenitors, Utf1 expression is shut off, accompanied by the gain of H3K27me3 (Fig.…”

Section: Small Functional Prc2 Recruiter Elements Cover Less Than Twomentioning

confidence: 99%

Short sequences can efficiently recruit histone H3 lysine 27 trimethylation in the absence of enhancer activity and DNA methylation

Jermann

Hoerner

Burger

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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127

129

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Significance Polycomb repressive complex 2 functions in gene repression and acts by methylating histone H3 at lysine 27 (H3K27me3). Despite its relevance, it remains elusive how this complex is recruited to its target sites in the genome. Here, we used repeated genomic targeting in embryonic stem cells to identify DNA sequence determinants that autonomously confer H3K27me3 recruitment. We show that surprisingly small CG-rich DNA sequences are sufficient to recruit H3K27me3, but only if they are devoid of DNA methylation and transcriptional activity. This study provides new insights into the mechanisms recruiting H3K27me3 and the cross-talk between diverse chromatin modifications.

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Chromatin measurements reveal contributions of synthesis and decay to steady‐state mRNA levels

Cited by 53 publications

References 77 publications

Analysis of intronic and exonic reads in RNA-seq data characterizes transcriptional and post-transcriptional regulation

Analysis of intronic and exonic reads in RNA-seq data characterizes transcriptional and post-transcriptional regulation

Immunoproteasomes Shape the Transcriptome and Regulate the Function of Dendritic Cells

Short sequences can efficiently recruit histone H3 lysine 27 trimethylation in the absence of enhancer activity and DNA methylation

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