Cis-regulatory elements explain most of the mRNA stability variation across genes in yeast

Cheng, Jianghua; Maier, Kerstin; Avsec, Žiga; Rus, Petra

doi:10.1101/085522

Cited by 12 publications

(21 citation statements)

References 78 publications

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“…In addition to transcription regulation, post-transcriptional regulation of mRNA stability is vital to the fine-tuning of mRNA abundance. To date, several mRNA-intrinsic properties, often in 5 0 or 3 0 untranslated regions (UTRs), have been shown to affect mRNA stability [4,5]. Due to the recent advances in technology, the contribution of mRNA stability to gene expression has been suggested [6].…”

Section: Introductionmentioning

confidence: 99%

Codon bias confers stability to humanmRNAs

et al. 2019

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Codon bias has been implicated as one of the major factors contributing to mRNA stability in several model organisms. However, the molecular mechanisms of codon bias on mRNA stability remain unclear in humans. Here, we show that human cells possess a mechanism to modulate RNA stability through a unique codon bias. Bioinformatics analysis showed that codons could be clustered into two distinct groups-codons with G or C at the third base position (GC3) and codons with either A or T at the third base position (AT3): the former stabilizing while the latter destabilizing mRNA. Quantification of codon bias showed that increased GC3-content entails proportionately higher GC-content. Through bioinformatics, ribosome profiling, and in vitro analysis, we show that decoupling the effects of codon bias reveals two modes of mRNA regulation, one GC3-and one GC-content dependent. Employing an immunoprecipitation-based strategy, we identify ILF2 and ILF3 as RNA-binding proteins that differentially regulate global mRNA abundances based on codon bias. Our results demonstrate that codon bias is a two-pronged system that governs mRNA abundance.

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Section: Introductionmentioning

confidence: 99%

Codon bias confers stability to humanmRNAs

et al. 2019

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“…To test if the observed dynamic range of gene expression levels is encoded in the DNA, we extracted the DNA sequence of the regulatory and coding regions of all the genes within one relative standard deviation of expression variation ( Fig 1B: 4238 genes with RSD < 1). A total of 2150 bp of regulatory sequences 9,11,14,15,[32][33][34][35][36] , 64 codon frequencies from coding regions 37 and additional 8 mRNA stability variables 13 , all known to be important for expression regulation, were used for prediction of mRNA levels ( Fig 1D, Fig S1-1, Methods).…”

Section: The Dynamic Range Of Gene Expression Levels Is Encoded In Thmentioning

confidence: 99%

“…mRNA transcription is controlled via the gene regulatory structure, comprised of coding and cis -regulatory regions that include promoters, untranslated regions (UTRs), and terminators each encoding a significant amount of information related to mRNA levels 9 . For instance, in Saccharomyces cerevisiae , properties of individual cis -regulatory regions can explain up to half of the variation in mRNA levels [10][11][12][13][14][15] . Considering that each part of the gene structure controls a specific process related to mRNA synthesis and decay 9,16,17 , as well as the overall transcription efficiency 18 , all gene parts must be concerted in perfectly timed execution in order to regulate expression.…”

Section: Introductionmentioning

confidence: 99%

Gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure

Zrimec

Buric

Muhammad

et al. 2019

Preprint

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Understanding the genetic regulatory code that governs gene expression is a primary, yet challenging aspiration in molecular biology that opens up possibilities to cure human diseases and solve biotechnology problems. However, the fundamental question of how each of the individual coding and non-coding regions of the gene regulatory structure interact and contribute to the mRNA expression levels remains unanswered. Considering that all the information for gene expression regulation is already present in living cells, here we applied deep learning on over 20,000 mRNA datasets to learn the genetic regulatory code controlling mRNA expression in 7 model organisms ranging from bacteria to human.We show that in all organisms, mRNA abundance can be predicted directly from the DNA sequence with high accuracy, demonstrating that up to 82% of the variation of gene expression levels is encoded in the gene regulatory structure. Coding and non-coding regions carry both overlapping and orthogonal information and additively contribute to gene expression levels. By searching for DNA regulatory motifs present across the whole gene regulatory structure, we discover that motif interactions can regulate gene expression levels in a range of over three orders of magnitude. The uncovered co-evolution of coding and non-coding regions challenges the current paradigm that single motifs or regions are solely responsible for gene expression levels. Instead, we propose a holistic system that spans all regions of the gene structure and is required to analyse, understand, and design any future gene expression systems.

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“…In addition to transcription regulation, post-transcriptional regulation of mRNA stability is vital to the fine-tuning of mRNA abundance. To date, several mRNA-intrinsic properties, often in 5′ or 3′ untranslated regions (UTR), have been shown to affect mRNA stability (4,5). Due to the recent advances in technology, the contribution of mRNA stability to gene expression has been suggested (6).…”

Section: Introductionmentioning

confidence: 99%

Codon Bias Confers Stability to mRNAs via ILF2 in Humans

Hia

Yang

Shichino

et al. 2019

Preprint

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Codon bias has been implicated as one of the major factors contributing to mRNA stability in several model organisms. However, the molecular mechanisms of codon bias on mRNA stability remain unclear in humans. Here, we show that human cells possess a mechanism to modulate RNA stability through a unique codon bias. Bioinformatics analysis showed that codons could be clustered into two distinct groups—codons with G or C at the third base position (GC3) and codons with either A or T at the third base position (AT3): the former stabilizing while the latter destabilizing mRNA. Quantification of codon bias showed that increased GC3‐content entails proportionately higher GC‐content. Through bioinformatics, ribosome profiling, and in vitro analysis, we show that decoupling the effects of codon bias reveals two modes of mRNA regulation, one GC3‐ and one GC‐content dependent. Employing an immunoprecipitation‐based strategy, we identify ILF2 and ILF3 as RNA‐binding proteins that differentially regulate global mRNA abundances based on codon bias. Our results demonstrate that codon bias is a two‐pronged system that governs mRNA abundance.

show abstract

Cis-regulatory elements explain most of the mRNA stability variation across genes in yeast

Abstract: 14The stability of mRNA is one of the major determinants of gene expression. Although

Cited by 12 publications

References 78 publications

Codon bias confers stability to humanmRNAs

Codon bias confers stability to humanmRNAs

Gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure

Codon Bias Confers Stability to mRNAs via ILF2 in Humans

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