Multiple Transcript Properties Related to Translation Affect mRNA Degradation Rates in Saccharomyces cerevisiae

A Chimeric ORF Fusion Phenotypic Reporter for Cryptococcus neoformans

The plethora of genome sequences produced in the postgenomic age has not resolved many of our most pressing biological questions. Correlating gene expression with an interrogatable and easily observable characteristic such as the surrogate phenotype conferred by a reporter gene is a valuable approach to gaining insight into gene function. Many reporters including lacZ, amdS, and the fluorescent proteins mRuby3 and mNeonGreen have been used across all manners of organisms. Described here is an investigation into the creation of a robust, synthetic, fusion reporter system for Cryptococcus neoformans that combines some of the most useful fluorophores available in this system with the versatility of the counter-selectable nature of amdS. The reporters generated include multiple composition and orientation variants, all of which were investigated for differences in expression. Evaluation of known promoters from the TEF1 and GAL7 genes was undertaken, elucidating novel expression tendencies of these biologically relevant C. neoformans regulators of transcription. Smaller than lacZ but providing multiple useful surrogate phenotypes for interrogation, the fusion ORF serves as a superior whole-cell assay compared to traditional systems. Ultimately, the work described here bolsters the array of relevant genetic tools that may be employed in furthering manipulation and understanding of the WHO fungal priority group pathogen C. neoformans.

Synthetic reversed sequences reveal default genomic states

Pervasive transcriptional activity is observed across diverse species. The genomes of extant organisms have undergone billions of years of evolution, making it unclear whether these genomic activities represent effects of selection or 'noise'1-4. Characterizing default genome states could help understand whether pervasive transcriptional activity has biological meaning. Here we addressed this question by introducing a synthetic 101-kb locus into the genomes of Saccharomyces cerevisiae and Mus musculus and characterizing genomic activity. The locus was designed by reversing but not complementing human HPRT1, including its flanking regions, thus retaining basic features of the natural sequence but ablating evolved coding or regulatory information. We observed widespread activity of both reversed and native HPRT1 loci in yeast, despite the lack of evolved yeast promoters. By contrast, the reversed locus displayed no activity at all in mouse embryonic stem cells, and instead exhibited repressive chromatin signatures. The repressive signature was alleviated in a locus variant lacking CpG dinucleotides; nevertheless, this variant was also transcriptionally inactive. These results show that synthetic genomic sequences that lack coding information are active in yeast, but inactive in mouse embryonic stem cells, consistent with a major difference in 'default genomic states' between these two divergent eukaryotic cell types, with implications for understanding pervasive transcription, horizontal transfer of genetic information and the birth of new genes.

Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom

In higher plants, the complexity of a system and the components within and among species are rapidly dissected by omics technologies. Multi-omics datasets are integrated to infer and enable a comprehensive understanding of the life processes of organisms of interest. Further, growing open-source datasets coupled with the emergence of high-performance computing and development of computational tools for biological sciences have assisted in silico functional prediction of unknown genes, proteins and metabolites, otherwise known as uncharacterized. The systems biology approach includes data collection and filtration, system modelling, experimentation and the establishment of new hypotheses for experimental validation. Informatics technologies add meaningful sense to the output generated by complex bioinformatics algorithms, which are now freely available in a user-friendly graphical user interface. These resources accentuate gene function prediction at a relatively minimal cost and effort. Herein, we present a comprehensive view of relevant approaches available for system-level gene function prediction in the plant kingdom. Together, the most recent applications and sought-after principles for gene mining are discussed to benefit the plant research community. A realistic tabulation of plant genomic resources is included for a less laborious and accurate candidate gene discovery in basic plant research and improvement strategies.

Anti-Inflammatory Activity of Ferula assafoetida Oleo-Gum-Resin (Asafoetida) against TNF-α-Stimulated Human Umbilical Vein Endothelial Cells (HUVECs)

Inflammation is the body’s biological reaction to endogenous and exogenous stimuli. Recent studies have demonstrated several anti-inflammatory properties of Ferula species. In this paper, we decided to study the anti-inflammatory effect of ethanolic extract of Ferula assafoetida oleo-gum-resin (asafoetida) against TNF-α-stimulated human umbilical vein endothelial cells (HUVECs). HUVECs were cultured in a flat-bottom plate and then treated with ethanolic extract of asafoetida (EEA, 0-500 μg/ml) and TNF-α (0-100 ng/ml) for 24 h. We used the MTT test to assess cell survival. In addition, the LC-MS analysis was performed to determine the active substances. HUVECs were pretreated with EEA and then induced by TNF-α. Intracellular reactive oxygen species (ROS) and adhesion of peripheral blood mononuclear cells (PBMCs) to HUVECs were evaluated with DCFH-DA and CFSE fluorescent probes, respectively. Gene expression of intercellular cell adhesion molecule-1 (ICAM-1), vascular cell adhesion molecule 1 (VCAM-1), and E-selectin and surface expression of ICAM-1 protein were measured using real-time PCR and flow cytometry methods, respectively. While TNF-α significantly increased intracellular ROS formation and PBMC adhesion to TNF-α-induced HUVECs, the pretreatment of HUVECs with EEA (125 and 250 μg/ml) significantly reduced the parameters. In addition, EEA pretreatment decreased TNF-α-induced mRNA expression of VCAM-1 and surface protein expression of ICAM-1 in the target cells. Taken together, the results indicated that EEA prevented ROS generation, triggered by TNF-α, and inhibited the expression of VCAM-1 and ICAM-1, leading to reduced PBMC adhesion. These findings suggest that EEA can probably have anti-inflammatory properties.

Controlling gene expression with deep generative design of regulatory DNA

Design of de novo synthetic regulatory DNA is a promising avenue to control gene expression in biotechnology and medicine. Using mutagenesis typically requires screening sizable random DNA libraries, which limits the designs to span merely a short section of the promoter and restricts their control of gene expression. Here, we prototype a deep learning strategy based on generative adversarial networks (GAN) by learning directly from genomic and transcriptomic data. Our ExpressionGAN can traverse the entire regulatory sequence-expression landscape in a gene-specific manner, generating regulatory DNA with prespecified target mRNA levels spanning the whole gene regulatory structure including coding and adjacent non-coding regions. Despite high sequence divergence from natural DNA, in vivo measurements show that 57% of the highly-expressed synthetic sequences surpass the expression levels of highly-expressed natural controls. This demonstrates the applicability and relevance of deep generative design to expand our knowledge and control of gene expression regulation in any desired organism, condition or tissue.

Design of de novo synthetic regulatory DNA is a promising avenue to control gene expression in biotechnology and medicine. Here the authors present EspressionGAN, a generative adversarial network that uses genomic and transcriptomic data to generate regulatory sequences.

Mitotic checkpoint gene expression is tuned by codon usage bias

The mitotic checkpoint (also called spindle assembly checkpoint, SAC) is a signaling pathway that safeguards proper chromosome segregation. Correct functioning of the SAC depends on adequate protein concentrations and appropriate stoichiometries between SAC proteins. Yet very little is known about the regulation of SAC gene expression. Here, we show in the fission yeast Schizosaccharomyces pombe that a combination of short mRNA half-lives and long protein half-lives supports stable SAC protein levels. For the SAC genes mad2+ and mad3+ , their short mRNA half-lives are caused, in part, by a high frequency of nonoptimal codons. In contrast, mad1+ mRNA has a short half-life despite a higher frequency of optimal codons, and despite the lack of known RNA-destabilizing motifs. Hence, different SAC genes employ different strategies of expression. We further show that Mad1 homodimers form co-translationally, which may necessitate a certain codon usage pattern. Taken together, we propose that the codon usage of SAC genes is fine-tuned to ensure proper SAC function. Our work shines light on gene expression features that promote spindle assembly checkpoint function and suggests that synonymous mutations may weaken the checkpoint.

Systematic In Vivo Characterization of Fluorescent Protein Maturation in Budding Yeast

Fluorescent protein (FP) maturation can limit the accuracy with which dynamic intracellular processes are captured and reduce the in vivo brightness of a given FP in fast-dividing cells. The knowledge of maturation timescales can therefore help users determine the appropriate FP for each application. However, in vivo maturation rates can greatly deviate from in vitro estimates that are mostly available. In this work, we present the first systematic study of in vivo maturation for 12 FPs in budding yeast. To overcome the technical limitations of translation inhibitors commonly used to study FP maturation, we implemented a new approach based on the optogenetic stimulations of FP expression in cells grown under constant nutrient conditions. Combining the rapid and orthogonal induction of FP transcription with a mathematical model of expression and maturation allowed us to accurately estimate maturation rates from microscopy data in a minimally invasive manner. Besides providing a useful resource for the budding yeast community, we present a new joint experimental and computational approach for characterizing FP maturation, which is applicable to a wide range of organisms.

Increased expression of the fluorescent reporter protein ymNeonGreen in Saccharomyces cerevisiae by reducing RNA secondary structure near the start codon

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Stable secondary RNA structure 3′ of the start codon inhibits ymNeonGreen expression.

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Removing secondary RNA structure increased expression in S. cerevisiae and E. coli.

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Expression was increased 2-fold in S. cerevisiae.

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Expression was increased 3.8-fold in E. coli.

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Increased expression in S. cerevisiae was promoter independent.

Expression of a new fluorescent reporter protein called mNeonGreen, that is not based on the jellyfish green fluorescent protein (GFP) sequence, shows increased brightness and folding speed compared to enhanced GFP. However, in vivo brightness of mNeonGreen and its yeast-optimized variant ymNeonGreen in S. cerevisiae is lower than expected, limiting the use of this high quantum yield, fast-folding reporter in budding yeast. This study shows that secondary RNA structure near the start codon in the ymNeonGreen ORF inhibits expression in S. cerevisiae. Removing secondary structure, without altering the ymNeonGreen protein sequence, led to a 2 and 4-fold increase in fluorescence when expressed in S. cerevisiae and E. coli, respectively. In S. cerevisiae, increased fluorescence was seen with strong and weak promoters and led to higher transcript levels suggesting greater transcript stability and improved expression in the absence of stable secondary RNA structure near the start codon.

Extracting novel hypotheses and findings from RNA-seq data

Over the past decade, improvements in technology and methods have enabled rapid and relatively inexpensive generation of high-quality RNA-seq datasets. These datasets have been used to characterize gene expression for several yeast species and have provided systems-level insights for basic biology, biotechnology and medicine. Herein, we discuss new techniques that have emerged and existing techniques that enable analysts to extract information from multifactorial yeast RNA-seq datasets. Ultimately, this minireview seeks to inspire readers to query datasets, whether previously published or freshly obtained, with creative and diverse methods to discover and support novel hypotheses.

Learning the Regulatory Code of Gene Expression

Data-driven machine learning is the method of choice for predicting molecular phenotypes from nucleotide sequence, modeling gene expression events including protein-DNA binding, chromatin states as well as mRNA and protein levels. Deep neural networks automatically learn informative sequence representations and interpreting them enables us to improve our understanding of the regulatory code governing gene expression. Here, we review the latest developments that apply shallow or deep learning to quantify molecular phenotypes and decode the cis-regulatory grammar from prokaryotic and eukaryotic sequencing data. Our approach is to build from the ground up, first focusing on the initiating protein-DNA interactions, then specific coding and non-coding regions, and finally on advances that combine multiple parts of the gene and mRNA regulatory structures, achieving unprecedented performance. We thus provide a quantitative view of gene expression regulation from nucleotide sequence, concluding with an information-centric overview of the central dogma of molecular biology.

The effects of codon bias and optimality on mRNA and protein regulation

The central dogma of molecular biology entails that genetic information is transferred from nucleic acid to proteins. Notwithstanding retro-transcribing genetic elements, DNA is transcribed to RNA which in turn is translated into proteins. Recent advancements have shown that each stage is regulated to control protein abundances for a variety of essential physiological processes. In this regard, mRNA regulation is essential in fine-tuning or calibrating protein abundances. In this review, we would like to discuss one of several mRNA-intrinsic features of mRNA regulation that has been gaining traction of recent-codon bias and optimality. Specifically, we address the effects of codon bias with regard to codon optimality in several biological processes centred on translation, such as mRNA stability and protein folding among others. Finally, we examine how different organisms or cell types, through this system, are able to coordinate physiological pathways to respond to a variety of stress or growth conditions.

RNA-GPS predicts high-resolution RNA subcellular localization and highlights the role of splicing

Subcellular localization is essential to RNA biogenesis, processing, and function across the gene expression life cycle. However, the specific nucleotide sequence motifs that direct RNA localization are incompletely understood. Fortunately, new sequencing technologies have provided transcriptome-wide atlases of RNA localization, creating an opportunity to leverage computational modeling. Here we present RNA-GPS, a new machine learning model that uses nucleotide-level features to predict RNA localization across eight different subcellular locations-the first to provide such a wide range of predictions. RNA-GPS's design enables high-throughput sequence ablation and feature importance analyses to probe the sequence motifs that drive localization prediction. We find localization informative motifs to be concentrated on 3'-UTRs and scattered along the coding sequence, and motifs related to splicing to be important drivers of predicted localization, even for cytotopic distinctions for membraneless bodies within the nucleus or for organelles within the cytoplasm. Overall, our results suggest transcript splicing is one of many elements influencing RNA subcellular localization.

Coding regions affect mRNA stability in human cells

A new paradigm has emerged that coding regions can regulate mRNA stability in model organisms. Here, due to differences in cognate tRNA abundance, synonymous codons are translated at different speeds, and slow codons then stimulate mRNA decay. To ask if this phenomenon also occurs in humans, we isolated RNA stability effects due to coding regions using the human ORFeome collection. We find that many open reading frame (ORF) characteristics, such as length and secondary structure, fail to provide explanations for how coding regions alter mRNA stability, and, instead, that the ORF relies on translation to impact mRNA stability. Consistent with what has been seen in other organisms, codon use is related to the effects of ORFs on transcript stability. Importantly, we found instability-associated codons have longer A-site dwell times, suggesting for the first time in humans a connection between elongation speed and mRNA decay. Thus, we propose that codon usage alters decoding speeds and so affects human mRNA stability.

Codon stabilization coefficient as a metric to gain insights into mRNA stability and codon bias and their relationships with translation

The codon stabilization coefficient (CSC) is derived from the correlation between each codon frequency in transcripts and mRNA half-life experimental data. In this work, we used this metric as a reference to compare previously published Saccharomyces cerevisiae mRNA half-life datasets and investigate how codon composition related to protein levels. We generated CSCs derived from nine studies. Four datasets produced similar CSCs, which also correlated with other independent parameters that reflected codon optimality, such as the tRNA abundance and ribosome residence time. By calculating the average CSC for each gene, we found that most mRNAs tended to have more non-optimal codons. Conversely, a high proportion of optimal codons was found for genes coding highly abundant proteins, including proteins that were only transiently overexpressed in response to stress conditions. We also used CSCs to identify and locate mRNA regions enriched in non-optimal codons. We found that these stretches were usually located close to the initiation codon and were sufficient to slow ribosome movement. However, in contrast to observations from reporter systems, we found no position-dependent effect on the mRNA half-life. These analyses underscore the value of CSCs in studies of mRNA stability and codon bias and their relationships with protein expression.

Codon bias confers stability to human mRNAs

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.

A Molecular Portrait of De Novo Genes in Yeasts

New genes, with novel protein functions, can evolve "from scratch" out of intergenic sequences. These de novo genes can integrate the cell's genetic network and drive important phenotypic innovations. Therefore, identifying de novo genes and understanding how the transition from noncoding to coding occurs are key problems in evolutionary biology. However, identifying de novo genes is a difficult task, hampered by the presence of remote homologs, fast evolving sequences and erroneously annotated protein coding genes. To overcome these limitations, we developed a procedure that handles the usual pitfalls in de novo gene identification and predicted the emergence of 703 de novo gene candidates in 15 yeast species from 2 genera whose phylogeny spans at least 100 million years of evolution. We validated 85 candidates by proteomic data, providing new translation evidence for 25 of them through mass spectrometry experiments. We also unambiguously identified the mutations that enabled the transition from noncoding to coding for 30 Saccharomyces de novo genes. We established that de novo gene origination is a widespread phenomenon in yeasts, only a few being ultimately maintained by selection. We also found that de novo genes preferentially emerge next to divergent promoters in GC-rich intergenic regions where the probability of finding a fortuitous and transcribed ORF is the highest. Finally, we found a more than 3-fold enrichment of de novo genes at recombination hot spots, which are GC-rich and nucleosome-free regions, suggesting that meiotic recombination contributes to de novo gene emergence in yeasts.

Translation elongation and mRNA stability are coupled through the ribosomal A-site

Messenger RNA (mRNA) degradation plays a critical role in regulating transcript levels in eukaryotic cells. Previous work by us and others has shown that codon identity exerts a powerful influence on mRNA stability. In Saccharomyces cerevisiae, studies using a handful of reporter mRNAs show that optimal codons increase translation elongation rate, which in turn increases mRNA stability. However, a direct relationship between elongation rate and mRNA stability has not been established across the entire yeast transcriptome. In addition, there is evidence from work in higher eukaryotes that amino acid identity influences mRNA stability, raising the question as to whether the impact of translation elongation on mRNA decay is at the level of tRNA decoding, amino acid incorporation, or some combination of each. To address these questions, we performed ribosome profiling of wild-type yeast. In good agreement with other studies, our data showed faster codon-specific elongation over optimal codons and faster transcript-level elongation correlating with transcript optimality. At both the codon-level and transcript-level, faster elongation correlated with increased mRNA stability. These findings were reinforced by showing increased translation efficiency and kinetics for a panel of 11 HIS3 reporter mRNAs of increasing codon optimality. While we did observe that elongation measured by ribosome profiling is composed of both amino acid identity and synonymous codon effects, further analyses of these data establish that A-site tRNA decoding rather than other steps of translation elongation is driving mRNA decay in yeast.

Systematic identification of factors mediating accelerated mRNA degradation in response to changes in environmental nitrogen

Cellular responses to changing environments frequently involve rapid reprogramming of the transcriptome. Regulated changes in mRNA degradation rates can accelerate reprogramming by clearing or stabilizing extant transcripts. Here, we measured mRNA stability using 4-thiouracil labeling in the budding yeast Saccharomyces cerevisiae during a nitrogen upshift and found that 78 mRNAs are subject to destabilization. These transcripts include Nitrogen Catabolite Repression (NCR) and carbon metabolism mRNAs, suggesting that mRNA destabilization is a mechanism for targeted reprogramming of the transcriptome. To explore the molecular basis of destabilization we implemented a SortSeq approach to screen the pooled deletion collection library for trans factors that mediate rapid GAP1 mRNA repression. We combined low-input multiplexed Barcode sequencing with branched-DNA single-molecule mRNA FISH and Fluorescence-activated cell sorting (BFF) to identify the Lsm1-7p/Pat1p complex and general mRNA decay machinery as important for GAP1 mRNA clearance. We also find that the decapping modulators EDC3 and SCD6, translation factor eIF4G2, and the 5' UTR of GAP1 are factors that mediate rapid repression of GAP1 mRNA, suggesting that translational control may impact the post-transcriptional fate of mRNAs in response to environmental changes.

The Interplay between the RNA Decay and Translation Machinery in Eukaryotes

RNA decay plays a major role in regulating gene expression and is tightly networked with other aspects of gene expression to effectively coordinate post-transcriptional regulation. The goal of this work is to provide an overview of the major factors and pathways of general messenger RNA (mRNA) decay in eukaryotic cells, and then discuss the effective interplay of this cytoplasmic process with the protein synthesis machinery. Given the transcript-specific and fluid nature of mRNA stability in response to changing cellular conditions, understanding the fundamental networking between RNA decay and translation will provide a foundation for a complete mechanistic understanding of this important aspect of cell biology.

Promoter architecture determines cotranslational regulation of mRNA

Information that regulates gene expression is encoded throughout each gene but if different regulatory regions can be understood in isolation, or if they interact, is unknown. Here we measure mRNA levels for 10,000 open reading frames (ORFs) transcribed from either an inducible or constitutive promoter. We find that the strength of cotranslational regulation on mRNA levels is determined by promoter architecture. By using a novel computational genetic screen of 6402 RNA-seq experiments, we identify the RNA helicase Dbp2 as the mechanism by which cotranslational regulation is reduced specifically for inducible promoters. Finally, we find that for constitutive genes, but not inducible genes, most of the information encoding regulation of mRNA levels in response to changes in growth rate is encoded in the ORF and not in the promoter. Thus, the ORF sequence is a major regulator of gene expression, and a nonlinear interaction between promoters and ORFs determines mRNA levels.

A Massively Parallel Reporter Assay of 3' UTR Sequences Identifies In Vivo Rules for mRNA Degradation

The stability of mRNAs is regulated by signals within their sequences, but a systematic and predictive understanding of the underlying sequence rules remains elusive. Here we introduce UTR-seq, a combination of massively parallel reporter assays and regression models, to survey the dynamics of tens of thousands of 3' UTR sequences during early zebrafish embryogenesis. UTR-seq revealed two temporal degradation programs: a maternally encoded early-onset program and a late-onset program that accelerated degradation after zygotic genome activation. Three signals regulated early-onset rates: stabilizing poly-U and UUAG sequences and destabilizing GC-rich signals. Three signals explained late-onset degradation: miR-430 seeds, AU-rich sequences, and Pumilio recognition sites. Sequence-based regression models translated 3' UTRs into their unique decay patterns and predicted the in vivo effect of sequence signals on mRNA stability. Their application led to the successful design of artificial 3' UTRs that conferred specific mRNA dynamics. UTR-seq provides a general strategy to uncover the rules of RNA cis regulation.

Shine-Dalgarno Sequences Play an Essential Role in the Translation of Plastid mRNAs in Tobacco

In prokaryotic systems, the translation initiation of many, though not all, mRNAs depends on interaction between a sequence element upstream of the start codon (the Shine-Dalgarno sequence [SD]) and a complementary sequence in the 3' end of the 16S rRNA (anti-Shine-Dalgarno sequence [aSD]). Although many chloroplast mRNAs harbor putative SDs in their 5' untranslated regions and the aSD displays strong conservation, the functional relevance of SD-aSD interactions in plastid translation is unclear. Here, by generating transplastomic tobacco (Nicotiana tabacum) mutants with point mutations in the aSD coupled with genome-wide analysis of translation by ribosome profiling, we provide a global picture of SD-dependent translation in plastids. We observed a pronounced correlation between weakened predicted SD-aSD interactions and reduced translation efficiency. However, multiple lines of evidence suggest that the strength of the SD-aSD interaction is not the only determinant of the translational output of many plastid mRNAs. Finally, the translation efficiency of mRNAs with strong secondary structures around the start codon is more dependent on the SD-aSD interaction than weakly structured mRNAs. Thus, our data reveal the importance of the aSD in plastid translation initiation, uncover chloroplast genes whose translation is influenced by SD-aSD interactions, and provide insights into determinants of translation efficiency in plastids.

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

The stability of mRNA is one of the major determinants of gene expression. Although a wealth of sequence elements regulating mRNA stability has been described, their quantitative contributions to half-life are unknown. Here, we built a quantitative model for Saccharomyces cerevisiae based on functional mRNA sequence features that explains 59% of the half-life variation between genes and predicts half-life at a median relative error of 30%. The model revealed a new destabilizing 3' UTR motif, ATATTC, which we functionally validated. Codon usage proves to be the major determinant of mRNA stability. Nonetheless, single-nucleotide variations have the largest effect when occurring on 3' UTR motifs or upstream AUGs. Analyzing mRNA half-life data of 34 knockout strains showed that the effect of codon usage not only requires functional decapping and deadenylation, but also the 5'-to-3' exonuclease Xrn1, the nonsense-mediated decay genes, but not no-go decay. Altogether, this study quantitatively delineates the contributions of mRNA sequence features on stability in yeast, reveals their functional dependencies on degradation pathways, and allows accurate prediction of half-life from mRNA sequence.

Wide-ranging and unexpected consequences of altered Pol II catalytic activity in vivo

Here we employ a set of RNA Polymerase II (Pol II) activity mutants to determine the consequences of increased or decreased Pol II catalysis on gene expression in Saccharomyces cerevisiae. We find that alteration of Pol II catalytic rate, either fast or slow, leads to decreased Pol II occupancy and apparent reduction in elongation rate in vivo. However, we also find that determination of elongation rate in vivo by chromatin immunoprecipitation can be confounded by the kinetics and conditions of transcriptional shutoff in the assay. We identify promoter and template-specific effects on severity of gene expression defects for both fast and slow Pol II mutants. We show that mRNA half-lives for a reporter gene are increased in both fast and slow Pol II mutant strains and the magnitude of half-life changes correlate both with mutants' growth and reporter expression defects. Finally, we tested a model that altered Pol II activity sensitizes cells to nucleotide depletion. In contrast to model predictions, mutated Pol II retains normal sensitivity to altered nucleotide levels. Our experiments establish a framework for understanding the diversity of transcription defects derived from altered Pol II activity mutants, essential for their use as probes of transcription mechanisms.

Pervasive yet nonuniform contributions of Dcp2 and Cnot7 to maternal mRNA clearance in zebrafish

mRNA degradation is a fundamental biological process that erases transcribed genetic information from cells. During maternal-to-zygotic transition of animal development, thousands of maternal mRNAs are degraded by multiple mechanisms including microRNAs and codon-mediated decay. Enzymatic requirements for maternal mRNA clearance, however, are not fully understood. Here, we analyzed a contribution of the decapping enzyme Dcp2 to maternal mRNA clearance in zebrafish by over-expressing catalytically inactive Dcp2 and performing RNA-seq analysis. As expected, Dcp2 had a widespread role in maternal mRNA clearance. Interestingly, each mRNA showed differential dependency on Dcp2-mediated decapping and Cnot7-mediated deadenylation for degradation. Correlation analysis identified several mRNA features that were associated with the observed differential dependency. Our results show pervasive yet nonuniform contributions of the decapping enzyme Dcp2 and the deadenylase Cnot7 to maternal mRNA clearance.

When mRNA translation meets decay

Messenger RNA (mRNA) translation and mRNA degradation are important determinants of protein output, and they are interconnected. Previously, it was thought that translation of an mRNA, as a rule, prevents its degradation. mRNA surveillance mechanisms, which degrade mRNAs as a consequence of their translation, were considered to be exceptions to this rule. Recently, however, it has become clear that many mRNAs are degraded co-translationally, and it has emerged that codon choice, by influencing the rate of ribosome elongation, affects the rate of mRNA decay. In this review, we discuss the links between translation and mRNA stability, with an emphasis on emerging data suggesting that codon optimality may regulate mRNA degradation.

Analysis of the association between codon optimality and mRNA stability in Schizosaccharomyces pombe

Background

Recent experiments have shown that codon optimality is a major determinant of mRNA stability in Saccharomyces cerevisiae and that this phenomenon may be conserved in Escherichia coli and some metazoans, although work in Neurospora crassa is not consistent with this model.

Results

We examined the association between codon optimality and mRNA stability in the fission yeast Schizosaccharomyces pombe. Our analysis revealed the following points. First, we observe a genome-wide association between codon optimality and mRNA stability also in S. pombe, suggesting evolutionary conservation of the phenomenon. Second, in both S. pombe and S. cerevisiae, mRNA synthesis rates are also correlated at the genome-wide analysis with codon optimality, suggesting that the long-appreciated association between codon optimality and mRNA abundance is due to regulation of both mRNA synthesis and degradation. However, when we examined correlation of codon optimality and either mRNA half-lives or synthesis rates controlling for mRNA abundance, codon optimality was still positively correlated with mRNA half-lives in S. cerevisiae, but the association was no longer significant for mRNA half-lives in S. pombe or for synthesis rates in either organism. This illustrates how only the pairwise analysis of multiple correlating variables may limit these types of analyses. Finally, in S. pombe, codon optimality is associated with known DNA/RNA sequence motifs that are associated with mRNA production/stability, suggesting these two features have been under similar selective pressures for optimal gene expression.

Conclusions

Consistent with the emerging body of studies, this study suggests that the association between codon optimality and mRNA stability may be a broadly conserved phenomenon. It also suggests that the association can be explained at least in part by independent adaptations of codon optimality and other transcript features for elevated expression during evolution.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3237-6) contains supplementary material, which is available to authorized users.