Multiplexed gene control reveals rapid mRNA turnover

Baudrimont, Antoine; Voegeli, Sylvia; Viloria, Eduardo Calero; Stritt, Fabian; Lénon, Marine; Wada, Takeo; Jaquet, Vincent; Becskei, Attila

doi:10.1126/sciadv.1700006

Cited by 88 publications

(109 citation statements)

References 61 publications

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“…As the correlation of PABA and mRNA stability is negative ( Figure S15) , Promoter Activity Buffering may be a property of stable transcripts, while Amplification may be a property of unstable transcripts. Cell-to-cell variability data for some genes are consistent with a change in mRNA stability ( Figure S15) , though steady-state protein levels are not sufficient to uniquely identify changes in mRNA stability (Baudrimont et al, 2017;Thattai, 2016;Zenklusen et al, 2008) . It is likely that many other biological processes, such as transcription elongation, transcription termination or protein-complex imbalance-mediated protein degradation can also play a role, and the precise biological factors that determine PABA will be unique to each gene.…”

Section: Titration Of Limiting Regulators Can Explain Paba In Synthetmentioning

confidence: 96%

Promoter activity buffering reduces the fitness cost of misregulation

Schikora-Tamarit

Salinas

Navasa

et al. 2017

Preprint

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Graphical AbstractIn Brief Systematic promoter replacement reveals that coding sequences and 3'UTRs play and active role in buffering cells from fitness defects due to misregulation. Highlights• The TF dose-response curve --how gene expression changes as a function of TF concentration --is encoded throughout the gene, not only in the promoter. Genes with the same promoter have different TF DRCs.• A coupled experimental system and mathematical model quantifies the intrinsic ability of genes to buffer or amplify promoter activity.• Promoter activity buffering reduces the effect of misregulation on fitness.. CC-BY-NC-ND 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/215186 doi: bioRxiv preprint first posted online Nov. 6, 2017; 2 Promoter activity buffering reduces the fitness cost of misregulation. Author Contributions Conceptualization Writing & figures Data analysis ExperimentsMiquel Àngel Schikora- Tamarit 1 Miquel Àngel Schikora- Tamarit 1 Miquel Àngel Schikora- Tamarit 1 Miquel Àngel Schikora- Tamarit Cells regulate gene expression by changing the concentration and activity of transcription factors (TFs). The response of each gene to changes in TF activity is generally assumed to be encoded in the promoter. Here we show that, even when the promoter itself remains constant, each gene has a unique TF dose response curve. Many genes have an intrinsic ability to either buffer or amplify the effects of high promoter activity. We present a coupled mathematical model and experimental system for quantifying this property. Promoter activity buffering can be encoded by sequences in both the open reading frame and 3'UTR and can be implemented by both autoregulatory feedback loops and by titration of limiting trans regulators. We show experimentally that promoter activity buffering insulates cells from fitness defects due to misexpression. The response of genes to changes in [TF] is encoded by sequences outside of the promoter, and this effect can either insulate or amplify the effects of aneuploidy and misregulation on organismal fitness. INTRODUCTIONUnderstanding how changes in transcription factor activity lead to changes in gene expression is essential for understanding how organisms regulate expression in response to external and internal signals, and for understanding how sequence variation in genomes affects phenotype Levo and Segal, 2014;López-Maury et al., 2008;Segal and Widom, 2009). Changes in gene expression are associated with clinically relevant phenotypes such as disease and differential response to drugs, and the current assumption is that much variation in phenotype and expression is due to sequence variation in classical regulatory regions such as enhancers and promoters (Albert and Kruglyak, 2015). However, current mathematical models of . CC-BY-NC-ND 4.0 International license It is made available unde...

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Section: Titration Of Limiting Regulators Can Explain Paba In Synthetmentioning

confidence: 96%

Promoter activity buffering reduces the fitness cost of misregulation

Schikora-Tamarit

Salinas

Navasa

et al. 2017

Preprint

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“…Survey of mRNA stability in dinoflagellates using in vivo labeling method revealed quite diverse mRNA half-life in Karenia brevis ranging from 42 min to 144 h [75]. However, a recent report demonstrated that mRNA degradation may occur rapidly with some genes start to degrade as early as 2 min [76]. Utilizing RNA stabilization solution such as RNAlater and RNAlater ICE seems effective to stop the dinoflagellates gene expression without compromising RNA integrity [46].…”

Section: Challenges and Consideration In Dinoflagellates Transcriptommentioning

confidence: 99%

RNA-Seq as an Emerging Tool for Marine Dinoflagellate Transcriptome Analysis: Process and Challenges

et al. 2018

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Abstract:Dinoflagellates are the large group of marine phytoplankton with primary studies interest regarding their symbiosis with coral reef and the abilities to form harmful algae blooms (HABs). Toxin produced by dinoflagellates during events of HABs cause severe negative impact both in the economy and health sector. However, attempts to understand the dinoflagellates genomic features are hindered by their complex genome organization. Transcriptomics have been employed to understand dinoflagellates genome structure, profile genes and gene expression. RNA-seq is one of the latest methods for transcriptomics study. This method is capable of profiling the dinoflagellates transcriptomes and has several advantages, including highly sensitive, cost effective and deeper sequence coverage. Thus, in this review paper, the current workflow of dinoflagellates RNA-seq starts with the extraction of high quality RNA and is followed by cDNA sequencing using the next-generation sequencing platform, dinoflagellates transcriptome assembly and computational analysis will be discussed. Certain consideration needs will be highlighted such as difficulty in dinoflagellates sequence annotation, post-transcriptional activity and the effect of RNA pooling when using RNA-seq.

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“…The program sums the CSC of ten codons and when 20 values equal o lower than -1.28 was found the program localized the position of the non-optimal codons 21 in the ORFs. 22 Young (Holstege et al, 1998), Brown (Wang et al, 2002), Hughes (Grigull et al, 2004), Coller (Presnyak et al, 2015), Struhl (Geisberg et al, 2014), Weis (Munchel et al, 2011), Gresham (Neymotin et al, 2014), Cramer (Sun et al, 2012) and Becskei (Baudrimont et al, 2017) CSCs of each of the datasets for the 61 yeast codons. Values range from -0.64 (negative correlation, green panels) to 1.00 (positive correlation, red panels).…”

Section: Localization Of Non-optimal Codons Stretches 16mentioning

confidence: 99%

“…Genome-2 wide mRNA half-life measurements showed poor correlation across different datasets and different 3 methods (and, in some cases, even between similar methods), classifying the same mRNA molecule as 4 stable and unstable. It makes the identification of stability sequence motifs and a global view of 5 transcription and translation dynamics in yeast a problematic task (Baudrimont et al, 2017;Harigaya and 6 Parker, 2016). Several features, such as mRNA secondary structure, sequence and structural elements 7 located within 5`and 3`UTR as well as the lengths of the transcripts can affect mRNA stability (Parker, 8 2012).…”

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confidence: 99%

Codon stabilization coefficient as an index for prediction of codon bias and its potential applications

Carneiro

Requião

Rossetto

et al. 2018

Preprint

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2Different methods of mRNA half-life measurements are available, but genome-wide measurements of 3 mRNA half-life in yeast showed a weak correlation between the methods. Moreover, when we compared 4 mRNA half-life determined by these methods with other cellular measurements such as mRNA and 5 protein abundance low correlation was found. To clarify this matter, we analyzed mRNA half-life datasets 6 from nine different groups to determine the most accurate method of measurement. Since codon 7 optimality is one of the significant determinants of mRNA stability, we used the codon stabilization 8 coefficient (CSC) as a reference for mRNA half-life measurement accuracy. After CSC calculation for 9 each dataset, we find strong positive correlations between the CSC from some datasets with other 10 parameters that reflect codon optimality such as tRNA abundance and ribosome residence time. By the 11 use of CSC parameter, we observed that most genes contain non-optimal codons and that codon bias 12 exists toward optimized and non-optimized genes. We also observed that stretches of non-optimal are not 13 randomly distributed since it causes impacts on translation. 14 15 16

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Multiplexed gene control reveals rapid mRNA turnover

Abstract: Researchers develop a new method to show that messenger RNA molecules live much shorter lives in the cell than previously thought.

Cited by 88 publications

References 61 publications

Promoter activity buffering reduces the fitness cost of misregulation

Promoter activity buffering reduces the fitness cost of misregulation

RNA-Seq as an Emerging Tool for Marine Dinoflagellate Transcriptome Analysis: Process and Challenges

Codon stabilization coefficient as an index for prediction of codon bias and its potential applications

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