Periodic <scp>mRNA</scp> synthesis and degradation co‐operate during cell cycle gene expression

Eser, Philipp; Demel, Carina; Maier, Kerstin; Schwalb, Björn; Pirkl, Nicole; Martin, Dietmar E.; Cramer, Patrick; Tresch, Achim

doi:10.1002/msb.134886

Cited by 85 publications

(90 citation statements)

References 55 publications

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“…Based on the prevalence of periodic oscillatory gene expression in cells and tissues of various organisms (Eser et al, 2014 (Imayoshi & Kageyama, 2014;Roese-Koerner, Stappert, & Brustle, 2017;Shimojo & Kageyama, 2016;Suzuki, Furusawa, & Kaneko, 2011;William et al, 2007), and tanycytes are now recognized as adult neural stem and progenitor cells because they can self-renew and also differentiate into neurons or astrocytes (Chaker et al, 2016;Haan et al, 2013;Lee et al, 2012;Robins et al, 2013;Xu et al, 2005). Thus, periodic gene expression in tanycytes may be a characteristic related specifically to their stem/progenitor cell properties, which is also supported by the fact that Prss56 is specific to stem or progenitor cells in the brain, retina and skin (…”

Section: Discussionmentioning

confidence: 99%

Prss56 expression in the rodent hypothalamus: Inverse correlation with pro‐opiomelanocortin suggests oscillatory gene expression in adult rat tanycytes

Wittmann

Lechan

2018

J of Comparative Neurology

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We recently reported that the number of hypothalamic tanycytes expressing proopiomelanocortin (Pomc) is highly variable among brains of adult rats. While its cause and significance remain unknown, identifying other variably expressed genes in tanycytes may help understand this curious phenomenon. In this in situ hybridization study, we report that the Prss56 gene, which encodes a trypsin-like serine protease and is expressed in neural stem/progenitor cells, shows a similarly variable mRNA expression in tanycytes of adult rats and correlates inversely with tanycyte Pomc mRNA. Prss56 was expressed in α1, β1, subsets of α2, and some median eminence γ tanycytes, but virtually absent from β2 tanycytes. Prss56 was also expressed in vimentin positive, tanycyte-like cells in the parenchyma of the ventromedial and arcuate nuclei, and in thyrotropin beta subunit-expressing cells of the pars tuberalis of the pituitary. In contrast to adults, Prss56 expression was uniformly high in tanycytes in adolescent rats. In mice, Prss56-expressing tanycytes and parenchymal cells were also observed but fewer in number and without significant variations. The results identify Prss56 as a second gene that is expressed variably in tanycytes of adult rats. We propose that the variable, inversely correlating expression of Prss56 and Pomc reflect periodically oscillating gene expression in tanycytes rather than stable expression levels that vary between individual rats. A possible functional link between Prss56 and POMC, and Prss56 as a potential marker for migrating tanycytes are discussed.

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Section: Discussionmentioning

confidence: 99%

Prss56 expression in the rodent hypothalamus: Inverse correlation with pro‐opiomelanocortin suggests oscillatory gene expression in adult rat tanycytes

Wittmann

Lechan

2018

J of Comparative Neurology

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“…Many biological processes are controlled by characteristic temporal changes in the concentrations of specific biomolecules. For instance, the cell cycle is accompanied by periodic changes in mRNA and protein expression [42][43][44] . Likewise, the circadian cycle 45 involves concerted changes in abundances of proteins, protein modifications, mRNAs and metabolites 46 .…”

Section: Box 3 Data Integrationmentioning

confidence: 99%

The Perseus computational platform for comprehensive analysis of (prote)omics data

et al. 2016

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A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.

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“…Fractionation-based methods (Churchman and Weissman, 2011; Pandya-Jones et al, 2013) may be impacted by non-specific RNA binding and co-precipitating proteins. Recently, several studies (Dolken et al, 2008; Eser et al, 2013; Rabani et al, 2011; Windhager et al, 2012) used short pulses of 4-thiouridine (4sU) RNA labeling to isolate newly-transcribed RNA and determine RNA kinetics during dynamic responses. Although most focused on RNA transcription and degradation, this strategy was also applied for RNA processing (Rabani et al, 2011; Windhager et al, 2012), albeit not dynamically.…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Sequencing and Modeling Identifies Distinct Dynamic RNA Regulatory Strategies

Rabani

Raychowdhury

Jovanović

et al. 2014

Cell

210

243

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Summary Cells control dynamic transitions in transcript levels by regulating transcription, processing and/or degradation through an integrated regulatory strategy. Here, we combine RNA metabolic labeling, rRNA-depleted RNA-seq, and DRiLL, a novel computational framework, to quantify the level, editing sites, and transcription, processing and degradation rates of each transcript at a splice junction resolution during the LPS response of mouse dendritic cells. Four key regulatory strategies, dominated by RNA transcription changes, generate most temporal gene expression patterns. Non-canonical strategies that also employ dynamic posttranscriptional regulation control only a minority of genes, but provide unique signal processing features. We validate Tristetraprolin (TTP) as a major regulator of RNA degradation in one non-canonical strategy. Applying DRiLL to the regulation of non-coding RNAs and to zebrafish embryogenesis demonstrates its broad utility. Our study provides a new quantitative approach to discover transcriptional and post-transcriptional events that control dynamic changes in transcript levels using RNA-Seq data.

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Periodic mRNA synthesis and degradation co‐operate during cell cycle gene expression

Cited by 85 publications

References 55 publications

Prss56 expression in the rodent hypothalamus: Inverse correlation with pro‐opiomelanocortin suggests oscillatory gene expression in adult rat tanycytes

Prss56 expression in the rodent hypothalamus: Inverse correlation with pro‐opiomelanocortin suggests oscillatory gene expression in adult rat tanycytes

The Perseus computational platform for comprehensive analysis of (prote)omics data

High-Resolution Sequencing and Modeling Identifies Distinct Dynamic RNA Regulatory Strategies

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