A comprehensive, quantitative, and genome-wide model of translation

Determining the Identity Nucleotides and the Energy of Binding of tRNAs to Their Aminoacyl-tRNA Synthetases Using a Simple Logistic Model

This study showed that the predictor in logistic regression can be applied to estimating the Gibbs free energy of tRNAs' recognition of and binding to their aminoacyl-tRNA synthetases. Then, 24 linear logistic regression models predicting different classes of tRNAs loaded with a corresponding amino acid were trained in a machine learning classification method, reducing the misclassification error to zero. The models were based on minimal subsets of Boolean explanatory variables describing the favorite presence of nucleotides or nucleosides localized in the different parts of the tRNA. In 90% of cases, they agree with the components of the consensus strand in a class of tRNAs loaded by a given amino acid. According to the proposed theoretical model, the values of the free energy for the entry of the recognition state in the process of tRNA charging were obtained, and the inputs from identity nucleotides and the tRNA strand backbone were distinguished. Almost all the resulting models indicated leading anticodon tandems defining the first and second positions of the anticodon (positions 35 and 36 of the tRNA strand) and the small sets (up to six positions) of the other nucleotides as the natural identity nucleotides most influential in the free energy balance. The magnitude of their input to this energy depends on the position in the strand, favoring positions -1, 35, and 36. The role of position 34 is relatively smaller. These identity attributes may not always be fully arranged in a real single adaptor molecule but were comprehensively present in a given tRNA class. A detailed analysis of the resulting models showed that the absolute value of the energy of binding the tandem 35-36 decreases with the number of identity positions, as well as with the decreasing number of possible hydrogen bonds. On the other hand, in these conditions, the absolute value of the energy of binding of other identity nucleotides increases. All the models indicate that the nucleotide-independent energy of the repulsion tRNA backbone decreases with the number of identity nucleotides. It was also shown that the total free energy change in entering the recognition state increases with the amino acid mass, making this process less spontaneous, which may have an evolutionary reference.

Modeling of mRNA deadenylation rates reveal a complex relationship between mRNA deadenylation and decay

Complete cytoplasmic polyadenosine tail (polyA-tail) deadenylation is thought to be essential for initiating mRNA decapping and subsequent degradation. To investigate this prevalent model, we conducted direct RNA sequencing of S. cerevisiae mRNAs derived from chase experiments under steady-state and stress condition. Subsequently, we developed a numerical model based on a modified gamma distribution function, which estimated the transcriptomic deadenylation rate at 10 A/min. A simplified independent method, based on the delineation of quantile polyA-tail values, showed a correlation between the decay and deadenylation rates of individual mRNAs, which appeared consistent within functional transcript groups and associated with codon optimality. Notably, these rates varied during the stress response. Detailed analysis of ribosomal protein-coding mRNAs (RPG mRNAs), constituting 40% of the transcriptome, singled out this transcript group. While deadenylation and decay of RPG mRNAs accelerated under heat stress, their degradation could proceed even when deadenylation was blocked, depending entirely on ongoing nuclear export. Our findings support the general primary function of deadenylation in dictating the onset of decapping, while also demonstrating complex relations between these processes.

Yeast Expressing a Phage Endolysin Reduces Endogenous Clostridium perfringens Ex Vivo in 21-Day-Old Broiler Chicken Intestinal Fluids

The phage endolysin PlyCP41 when purified from Escherichia coli exhibits lytic activity against Clostridium perfringens (CP) in vitro. The anti-clostridial activity of PlyCP41 endolysin expressed in transgenic yeast (Saccharomyces cerevisiae) was verified in phosphate buffered saline via mixing experiments with cultured CP and transgenic yeast slurries followed by serial dilution plating and colony counts on tryptose sulfite cycloserine (CP indicator) plates. The transgenic yeast containing PlyCP41 resulted in a log10 4.5 reduction (99.997%; P < 0.01) of the cultured CP. In addition, this serial dilution plating assay was used to demonstrate that transgenic yeast slurries could reduce the endogenous CP content in fluids from three different gastrointestinal regions (proximal, medial, and distal) from 21-day-old broiler chickens. The transgenic yeast treatment of gut slurries resulted in a log 10 1.19, 4.53, and 1.28 reduction in proximal, medial, and distal gut slurries (90% to 99.99% of the endogenous CP; P < 0.01), respectively, compared to nontreatment controls. These results indicate that the phage endolysin PlyCP41 expressed in S. cerevisiae is effective at reducing the endogenous CP in gastrointestinal fluids of broiler chickens. Future studies will measure the anti-CP effect in vivo by administering transgenic yeast to broiler chickens in the feed.

Comprehensive quantitative modeling of translation efficiency in a genome-reduced bacterium

Translation efficiency has been mainly studied by ribosome profiling, which only provides an incomplete picture of translation kinetics. Here, we integrated the absolute quantifications of tRNAs, mRNAs, RNA half-lives, proteins, and protein half-lives with ribosome densities and derived the initiation and elongation rates for 475 genes (67% of all genes), 73 with high precision, in the bacterium Mycoplasma pneumoniae (Mpn). We found that, although the initiation rate varied over 160-fold among genes, most of the known factors had little impact on translation efficiency. Local codon elongation rates could not be fully explained by the adaptation to tRNA abundances, which varied over 100-fold among tRNA isoacceptors. We provide a comprehensive quantitative view of translation efficiency, which suggests the existence of unidentified mechanisms of translational regulation in Mpn.

Physical modeling of ribosomes along messenger RNA: Estimating kinetic parameters from ribosome profiling experiments using a ballistic model

Gene expression is the synthesis of proteins from the information encoded on DNA. One of the two main steps of gene expression is the translation of messenger RNA (mRNA) into polypeptide sequences of amino acids. Here, by taking into account mRNA degradation, we model the motion of ribosomes along mRNA with a ballistic model where particles advance along a filament without excluded volume interactions. Unidirectional models of transport have previously been used to fit the average density of ribosomes obtained by the experimental ribo-sequencing (Ribo-seq) technique in order to obtain the kinetic rates. The degradation rate is not, however, accounted for and experimental data from different experiments are needed to have enough parameters for the fit. Here, we propose an entirely novel experimental setup and theoretical framework consisting in splitting the mRNAs into categories depending on the number of ribosomes from one to four. We solve analytically the ballistic model for a fixed number of ribosomes per mRNA, study the different regimes of degradation, and propose a criterion for the quality of the inverse fit. The proposed method provides a high sensitivity to the mRNA degradation rate. The additional equations coming from using the monosome (single ribosome) and polysome (arbitrary number) ribo-seq profiles enable us to determine all the kinetic rates in terms of the experimentally accessible mRNA degradation rate.

Translation factor and RNA binding protein mRNA interactomes support broader RNA regulons for posttranscriptional control

The regulation of translation provides a rapid and direct mechanism to modulate the cellular proteome. In eukaryotes, an established model for the recruitment of ribosomes to mRNA depends upon a set of conserved translation initiation factors. Nevertheless, how cells orchestrate and define the selection of individual mRNAs for translation, as opposed to other potential cytosolic fates, is poorly understood. We have previously found significant variation in the interaction between individual mRNAs and an array of translation initiation factors. Indeed, mRNAs can be separated into different classes based upon these interactions to provide a framework for understanding different modes of translation initiation. Here, we extend this approach to include new mRNA interaction profiles for additional proteins involved in shaping the cytoplasmic fate of mRNAs. This work defines a set of seven mRNA clusters, based on their interaction profiles with 12 factors involved in translation and/or RNA binding. The mRNA clusters share both physical and functional characteristics to provide a rationale for the interaction profiles. Moreover, a comparison with mRNA interaction profiles from a host of RNA binding proteins suggests that there are defined patterns in the interactions of functionally related mRNAs. Therefore, this work defines global cytoplasmic mRNA binding modules that likely coordinate the synthesis of functionally related proteins.

Homeostatic regulation of ribosomal proteins by ubiquitin-independent cotranslational degradation

Ribosomes are the workplace for protein biosynthesis. Protein production required for normal cell function is tightly linked to ribosome abundance. It is well known that ribosomal genes are actively transcribed and ribosomal messenger RNAs (mRNAs) are rapidly translated, and yet ribosomal proteins have relatively long half-lives. These observations raise questions as to how homeostasis of ribosomal proteins is controlled. Here, we show that ribosomal proteins, while posttranslationally stable, are subject to high-level cotranslational protein degradation (CTPD) except for those synthesized as ubiquitin (Ub) fusion precursors. The N-terminal Ub moiety protects fused ribosomal proteins from CTPD. We further demonstrate that cotranslational folding efficiency and expression level are two critical factors determining CTPD of ribosomal proteins. Different from canonical posttranslational degradation, we found that CTPD of all the ribosomal proteins tested in this study does not require prior ubiquitylation. This work provides insights into the regulation of ribosomal protein homeostasis and furthers our understanding of the mechanism and biological significance of CTPD.

Cellular and environmental dynamics influence species-specific extents of organelle gene retention

Mitochondria and plastids rely on many nuclear-encoded genes, but retain small subsets of the genes they need to function in their own organelle DNA (oDNA). Different species retain different numbers of oDNA genes, and the reasons for these differences are not completely understood. Here, we use a mathematical model to explore the hypothesis that the energetic demands imposed by an organism's changing environment influence how many oDNA genes it retains. The model couples the physical biology of cell processes of gene expression and transport to a supply-and-demand model for the environmental dynamics to which an organism is exposed. The trade-off between fulfilling metabolic and bioenergetic environmental demands, and retaining genetic integrity, is quantified for a generic gene encoded either in oDNA or in nuclear DNA. Species in environments with high-amplitude, intermediate-frequency oscillations are predicted to retain the most organelle genes, whereas those in less dynamic or noisy environments the fewest. We discuss support for, and insight from, these predictions with oDNA data across eukaryotic taxa, including high oDNA gene counts in sessile organisms exposed to day-night and intertidal oscillations (including plants and algae) and low counts in parasites and fungi.

A dynamical stochastic model of yeast translation across the cell cycle

Translation is a central step in gene expression, however its quantitative and time-resolved regulation is poorly understood. We developed a discrete, stochastic model for protein translation in S. cerevisiae in a whole-transcriptome, single-cell context. A "base case" scenario representing an average cell highlights translation initiation rates as the main co-translational regulatory parameters. Codon usage bias emerges as a secondary regulatory mechanism through ribosome stalling. Demand for anticodons with low abundancy is shown to cause above-average ribosome dwelling times. Codon usage bias correlates strongly both with protein synthesis rates and elongation rates. Applying the model to a time-resolved transcriptome estimated by combining data from FISH and RNA-Seq experiments, it could be shown that increased total transcript abundance during the cell cycle decreases translation efficiency at single transcript level. Translation efficiency grouped by gene function shows highest values for ribosomal and glycolytic genes. Ribosomal proteins peak in S phase while glycolytic proteins rank highest in later cell cycle phases.

Estimating Total Quantitative Protein Content in Escherichia coli, Saccharomyces cerevisiae, and HeLa Cells

The continuous improvement of proteomic techniques, most notably mass spectrometry, has generated quantified proteomes of many organisms with unprecedented depth and accuracy. However, there is still a significant discrepancy in the reported numbers of total protein molecules per specific cell type. In this article, we explore the results of proteomic studies of Escherichia coli, Saccharomyces cerevisiae, and HeLa cells in terms of total protein copy numbers per cell. We observe up to a ten-fold difference between reported values. Investigating possible reasons for this discrepancy, we conclude that neither an unmeasured fraction of the proteome nor biases in the quantification of individual proteins can explain the observed discrepancy. We normalize protein copy numbers in each study using a total protein amount per cell as reported in the literature and create integrated proteome maps of the selected model organisms. Our results indicate that cells contain from one to three million protein molecules per µm³ and that protein copy density decreases with increasing organism complexity.

BD Rhapsody™ Single-Cell Analysis System Workflow: From Sample to Multimodal Single-Cell Sequencing Data

Advancements in single-cell sequencing have revolutionized our understanding of complex biological systems such as the immune system and allowed us to overcome limitations in various disciplines of life science research such as oncology, developmental biology, or neurobiology (Perkel, Nature 595. https://www.nature.com/articles/d41586-021-01994-w , 2021).The BD Rhapsody™ Single-Cell Analysis System enables us to capture multimodal information from thousands of single cells in parallel ("Multiomics") covering mRNA expression levels, protein expression levels, the immune repertoire for T-cell receptors (TCR) and B-cell receptors (BCR), and the identification of antigen-specific T cells and B cells using dCODE Dextramer^® (RiO) from Immudex. The system utilizes microwell-based cartridges that allow to capture a broad range of single cells and an imaging device for sample quality control and workflow quality control (including viability and multiplets). The power of Multiomics relies on simultaneously measuring several aspects of single cells, including gene expression and protein abundance, using next generation sequencing (NGS) as a single readout.Here we describe the complete BD Rhapsody™ Single-Cell Analysis System from the sample preparation including different options for the antibody and/or dCODE Dextramer^® staining through to the data analysis.For updated protocols, guides, and technical bulletins, please visit the BD Scomix page: https://scomix.bd.com/hc/en-us or the BDB webpage: https://www.bdbiosciences.com/en-eu .

Rapid 40S scanning and its regulation by mRNA structure during eukaryotic translation initiation

How the eukaryotic 43S preinitiation complex scans along the 5' untranslated region (5' UTR) of a capped mRNA to locate the correct start codon remains elusive. Here, we directly track yeast 43S-mRNA binding, scanning, and 60S subunit joining by real-time single-molecule fluorescence spectroscopy. 43S engagement with mRNA occurs through a slow, ATP-dependent process driven by multiple initiation factors including the helicase eIF4A. Once engaged, 43S scanning occurs rapidly and directionally at ∼100 nucleotides per second, independent of multiple cycles of ATP hydrolysis by RNA helicases post ribosomal loading. Scanning ribosomes can proceed through RNA secondary structures, but 5' UTR hairpin sequences near start codons drive scanning ribosomes at start codons backward in the 5' direction, requiring rescanning to arrive once more at a start codon. Direct observation of scanning ribosomes provides a mechanistic framework for translational regulation by 5' UTR structures and upstream near-cognate start codons.

Ribosome Elongation Kinetics of Consecutively Charged Residues Are Coupled to Electrostatic Force

The speed of protein synthesis can dramatically change when consecutively charged residues are incorporated into an elongating nascent protein by the ribosome. The molecular origins of this class of allosteric coupling remain unknown. We demonstrate, using multiscale simulations, that positively charged residues generate large forces that move the P-site amino acid away from the A-site amino acid. Negatively charged residues generate forces of similar magnitude but move the A- and P-sites closer together. These conformational changes, respectively, increase and decrease the transition state barrier height to peptide bond formation, explaining how charged residues mechanochemically alter translation speed. This mechanochemical mechanism is consistent with in vivo ribosome profiling data exhibiting proportionality between translation speed and the number of charged residues, experimental data characterizing nascent chain conformations, and a previously published cryo-EM structure of a ribosome-nascent chain complex containing consecutive lysines. These results expand the role of mechanochemistry in translation and provide a framework for interpreting experimental results on translation speed.

Stress granules display bistable dynamics modulated by Cdk

Stress granules (SGs) are conserved biomolecular condensates that originate in response to many stress conditions. These membraneless organelles contain nontranslating mRNAs and a diverse subproteome, but our knowledge of their regulation and functional relevance is still incipient. Here, we describe a mutual-inhibition interplay between SGs and Cdc28, the budding yeast Cdk. Among Cdc28 interactors acting as negative modulators of Start, we have identified Whi8, an RNA-binding protein that localizes to SGs and recruits the mRNA of CLN3, the most upstream G1 cyclin, for efficient translation inhibition and Cdk inactivation under stress. However, Whi8 also contributes to recruiting Cdc28 to SGs, where it acts to promote their dissolution. As predicted by a mutual-inhibition framework, the SG constitutes a bistable system that is modulated by Cdk. Since mammalian cells display a homologous mechanism, we propose that the opposing functions of specific mRNA-binding proteins and Cdk’s subjugate SG dynamics to a conserved hysteretic switch.

Global view on the metabolism of RNA poly(A) tails in yeast Saccharomyces cerevisiae

The polyadenosine tail (poly[A]-tail) is a universal modification of eukaryotic messenger RNAs (mRNAs) and non-coding RNAs (ncRNAs). In budding yeast, Pap1-synthesized mRNA poly(A) tails enhance export and translation, whereas Trf4/5-mediated polyadenylation of ncRNAs facilitates degradation by the exosome. Using direct RNA sequencing, we decipher the extent of poly(A) tail dynamics in yeast defective in all relevant exonucleases, deadenylases, and poly(A) polymerases. Predominantly ncRNA poly(A) tails are 20-60 adenosines long. Poly(A) tails of newly transcribed mRNAs are 50 adenosine long on average, with an upper limit of 200. Exonucleolysis by Trf5-assisted nuclear exosome and cytoplasmic deadenylases trim the tails to 40 adenosines on average. Surprisingly, PAN2/3 and CCR4-NOT deadenylase complexes have a large pool of non-overlapping substrates mainly defined by expression level. Finally, we demonstrate that mRNA poly(A) tail length strongly responds to growth conditions, such as heat and nutrient deprivation.

Computational Resources and Strategies to Construct Single-Molecule Models of FISH

Currently, the interactions occurring between oligonucleotides and the cellular envelope of bacteria are not fully resolved at the molecular level. Understanding these interactions is essential to gain insights on how to improve the internalization of the tagged oligonucleotides during fluorescence in situ hybridization (FISH). Agent-based modeling (ABM) is a promising in silico tool to dynamically simulate FISH and bring forward new knowledge on this process. Notably, it is important to simulate the whole bacterial cell, including the different layers of the cell envelope, given that the oligonucleotide must cross the envelope to reach its target in the cytosol. In addition, it is also important to characterize other molecules in the cell to best emulate the cell and represent molecular crowding. Here, we review the main information that should be compiled to construct an ABM on FISH and provide a practical example of an oligonucleotide targeting the 23S rRNA of Escherichia coli .

Modelling the effect of ribosome mobility on the rate of protein synthesis

Translation is one of the main steps in the synthesis of proteins. It consists of ribosomes that translate sequences of nucleotides encoded on mRNA into polypeptide sequences of amino acids. Ribosomes bound to mRNA move unidirectionally, while unbound ribosomes diffuse in the cytoplasm. It has been hypothesized that finite diffusion of ribosomes plays an important role in ribosome recycling and that mRNA circularization enhances the efficiency of translation, see e.g. Lodish et al. (Molecular cell biology, 8th edn, W.H. Freeman and Company, San Francisco, 2016). In order to estimate the effect of cytoplasmic diffusion on the rate of translation, we consider a totally asymmetric simple exclusion process coupled to a finite diffusive reservoir, which we call the ribosome transport model with diffusion. In this model, we derive an analytical expression for the rate of protein synthesis as a function of the diffusion constant of ribosomes, which is corroborated with results from continuous-time Monte Carlo simulations. Using a wide range of biological relevant parameters, we conclude that diffusion is not a rate limiting factor in translation initiation because diffusion is fast enough in biological cells.

A Trans-Omics Comparison Reveals Common Gene Expression Strategies in Four Model Organisms and Exposes Similarities and Differences between Them

The ultimate goal of gene expression regulation is on the protein level. However, because the amounts of mRNAs and proteins are controlled by their synthesis and degradation rates, the cellular amount of a given protein can be attained by following different strategies. By studying omics data for six expression variables (mRNA and protein amounts, plus their synthesis and decay rates), we previously demonstrated the existence of common expression strategies (CESs) for functionally related genes in the yeast Saccharomyces cerevisiae. Here we extend that study to two other eukaryotes: the yeast Schizosaccharomyces pombe and cultured human HeLa cells. We also use genomic data from the model prokaryote Escherichia coli as an external reference. We show that six-variable profiles (6VPs) can be constructed for every gene and that these 6VPs are similar for genes with similar functions in all the studied organisms. The differences in 6VPs between organisms can be used to establish their phylogenetic relationships. The analysis of the correlations among the six variables supports the hypothesis that most gene expression control occurs in actively growing organisms at the transcription rate level, and that translation plays a minor role. We propose that living organisms use CESs for the genes acting on the same physiological pathways, especially for those belonging to stable macromolecular complexes, but CESs have been modeled by evolution to adapt to the specific life circumstances of each organism.

Stochastic thermodynamics and modes of operation of a ribosome: A network theoretic perspective

The ribosome is one of the largest and most complex macromolecular machines in living cells. It polymerizes a protein in a step-by-step manner as directed by the corresponding nucleotide sequence on the template messenger RNA (mRNA) and this process is referred to as "translation" of the genetic message encoded in the sequence of mRNA transcript. In each successful chemomechanical cycle during the (protein) elongation stage, the ribosome elongates the protein by a single subunit, called amino acid, and steps forward on the template mRNA by three nucleotides called a codon. Therefore, a ribosome is also regarded as a molecular motor for which the mRNA serves as the track, its step size is that of a codon and two molecules of GTP and one molecule of ATP hydrolyzed in that cycle serve as its fuel. What adds further complexity is the existence of competing pathways leading to distinct cycles, branched pathways in each cycle, and futile consumption of fuel that leads neither to elongation of the nascent protein nor forward stepping of the ribosome on its track. We investigate a model formulated in terms of the network of discrete chemomechanical states of a ribosome during the elongation stage of translation. The model is analyzed using a combination of stochastic thermodynamic and kinetic analysis based on a graph-theoretic approach. We derive the exact solution of the corresponding master equations. We represent the steady state in terms of the cycles of the underlying network and discuss the energy transduction processes. We identify the various possible modes of operation of a ribosome in terms of its average velocity and mean rate of GTP hydrolysis. We also compute entropy production as functions of the rates of the interstate transitions and the thermodynamic cost for accuracy of the translation process.

Rqc1 and other yeast proteins containing highly positively charged sequences are not targets of the RQC complex

Previous work has suggested that highly positively charged protein segments coded by rare codons or poly (A) stretches induce ribosome stalling and translational arrest through electrostatic interactions with the negatively charged ribosome exit tunnel, leading to inefficient elongation. This arrest leads to the activation of the Ribosome Quality Control (RQC) pathway and results in low expression of these reporter proteins. However, the only endogenous yeast proteins known to activate the RQC are Rqc1, a protein essential for RQC function, and Sdd1, a protein with unknown function, both of which contain polybasic sequences. To explore the generality of this phenomenon, we investigated whether the RQC complex controls the expression of other proteins with polybasic sequences. We showed by ribosome profiling data analysis and western blot that proteins containing polybasic sequences similar to, or even more positively charged than those of Rqc1 and Sdd1, were not targeted by the RQC complex. We also observed that the previously reported Ltn1-dependent regulation of Rqc1 is posttranslational, independent of the RQC activity. Taken together, our results suggest that RQC should not be regarded as a general regulatory pathway for the expression of highly positively charged proteins in yeast.

Inferring efficiency of translation initiation and elongation from ribosome profiling

One of the main goals of ribosome profiling is to quantify the rate of protein synthesis at the level of translation. Here, we develop a method for inferring translation elongation kinetics from ribosome profiling data using recent advances in mathematical modelling of mRNA translation. Our method distinguishes between the elongation rate intrinsic to the ribosome’s stepping cycle and the actual elongation rate that takes into account ribosome interference. This distinction allows us to quantify the extent of ribosomal collisions along the transcript and identify individual codons where ribosomal collisions are likely. When examining ribosome profiling in yeast, we observe that translation initiation and elongation are close to their optima and traffic is minimized at the beginning of the transcript to favour ribosome recruitment. However, we find many individual sites of congestion along the mRNAs where the probability of ribosome interference can reach \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$50\%$\end{document}. Our work provides new measures of translation initiation and elongation efficiencies, emphasizing the importance of rating these two stages of translation separately.

Computational discovery and modeling of novel gene expression rules encoded in the mRNA

The transcript is populated with numerous overlapping codes that regulate all steps of gene expression. Deciphering these codes is very challenging due to the large number of variables involved, the non-modular nature of the codes, biases and limitations in current experimental approaches, our limited knowledge in gene expression regulation across the tree of life, and other factors. In recent years, it has been shown that computational modeling and algorithms can significantly accelerate the discovery of novel gene expression codes. Here, we briefly summarize the latest developments and different approaches in the field.

Computational resources and strategies to assess single-molecule dynamics of the translation process in S. cerevisiae

This work provides a systematic and comprehensive overview of available resources for the molecular-scale modelling of the translation process through agent-based modelling. The case study is the translation in Saccharomyces cerevisiae, one of the most studied yeasts. The data curation workflow encompassed structural information about the yeast (i.e. the simulation environment), and the proteins, ribonucleic acids and other types of molecules involved in the process (i.e. the agents). Moreover, it covers the main process events, such as diffusion (i.e. motion of molecules in the environment) and collision efficiency (i.e. interaction between molecules). Data previously determined by wet-lab techniques were preferred, resorting to computational predictions/extrapolations only when strictly necessary. The computational modelling of the translation processes is of added industrial interest, since it may bring forward knowledge on how to control such phenomena and enhance the production of proteins of interest in a faster and more efficient manner.

Optimizing the dynamics of protein expression

Heterologously expressed genes require adaptation to the host organism to ensure adequate levels of protein synthesis, which is typically approached by replacing codons by the target organism’s preferred codons. In view of frequently encountered suboptimal outcomes we introduce the codon-specific elongation model (COSEM) as an alternative concept. COSEM simulates ribosome dynamics during mRNA translation and informs about protein synthesis rates per mRNA in an organism- and context-dependent way. Protein synthesis rates from COSEM are integrated with further relevant covariates such as translation accuracy into a protein expression score that we use for codon optimization. The scoring algorithm further enables fine-tuning of protein expression including deoptimization and is implemented in the software OCTOPOS. The protein expression score produces competitive predictions on proteomic data from prokaryotic, eukaryotic, and human expression systems. In addition, we optimized and tested heterologous expression of manA and ova genes in Salmonella enterica serovar Typhimurium. Superiority over standard methodology was demonstrated by a threefold increase in protein yield compared to wildtype and commercially optimized sequences.

Genome scale analysis of Escherichia coli with a comprehensive prokaryotic sequence-based biophysical model of translation initiation and elongation

Translation initiation in prokaryotes is affected by the mRNA folding and interaction of the ribosome binding site with the ribosomal RNA. The elongation rate is affected, among other factors, by the local biophysical properties of the coding regions, the decoding rates of different codons, and the interactions among ribosomes. Currently, there is no comprehensive biophysical model of translation that enables the prediction of mRNA translation dynamics based only on the transcript sequence and while considering all of these fundamental aspects of translation. In this study, we provide, for the first time, a computational simulative biophysical model of both translation initiation and elongation with all aspects mentioned above. We demonstrate our model performance and advantages focusing on Escherichia coli genes. We further show that the model enables prediction of translation rate, protein levels, and ribosome densities. In addition, our model enables quantifying the effect of silent mutations on translation rate in different parts of the transcript, the relative effect of mutations on translation initiation and elongation, and the effect of mutations on ribosome traffic jams. Thus, unlike previous models, the proposed one provides comprehensive information, facilitating future research in disciplines such as molecular evolution, synthetic biology, and functional genomics. A toolkit to estimate translation dynamics of transcripts is available at: https://www.cs.tau.ac.il/∼tamirtul/transim.

Formulation of the protein synthesis rate with sequence information

Translation is a central biological process by which proteins are synthesized from genetic information contained within mRNAs. Here, we investigate the kinetics of translation at the molecular level by a stochastic simulation model. The model explicitly includes RNA sequences, ribosome dynamics, the tRNA pool and biochemical reactions involved in the translation elongation. The results show that the translation efficiency is mainly limited by the available ribosome number, translation initiation and the translation elongation time. The elongation time is a log-normal distribution, with the mean and variance determined by the codon saturation and the process of aa-tRNA selection at each codon binding site. Moreover, our simulations show that the translation accuracy exponentially decreases with the sequence length. These results suggest that aa-tRNA competition is crucial for both translation elongation, translation efficiency and the accuracy, which in turn determined the effective protein production rate of correct proteins. Our results improve the dynamical equation of protein production with a delay differential equation that is dependent on sequence information through both the effective production rate and the distribution of elongation time.

Determinants of translation speed are randomly distributed across transcripts resulting in a universal scaling of protein synthesis times

Ribosome profiling experiments have found greater than 100-fold variation in ribosome density along mRNA transcripts, indicating that individual codon elongation rates can vary to a similar degree. This wide range of elongation times, coupled with differences in codon usage between transcripts, suggests that the average codon translation-rate per gene can vary widely. Yet, ribosome run-off experiments have found that the average codon translation rate for different groups of transcripts in mouse stem cells is constant at 5.6 AA/s. How these seemingly contradictory results can be reconciled is the focus of this study. Here, we combine knowledge of the molecular factors shown to influence translation speed with genomic information from Escherichia coli, Saccharomyces cerevisiae and Homo sapiens to simulate the synthesis of cytosolic proteins in these organisms. The model recapitulates a near constant average translation rate, which we demonstrate arises because the molecular determinants of translation speed are distributed nearly randomly amongst most of the transcripts. Consequently, codon translation rates are also randomly distributed and fast-translating segments of a transcript are likely to be offset by equally probable slow-translating segments, resulting in similar average elongation rates for most transcripts. We also show that the codon usage bias does not significantly affect the near random distribution of codon translation rates because only about 10% of the total transcripts in an organism have high codon usage bias while the rest have little to no bias. Analysis of Ribo-Seq data and an in vivo fluorescent assay supports these conclusions.

Gene length as a regulator for ribosome recruitment and protein synthesis: theoretical insights

Protein synthesis rates are determined, at the translational level, by properties of the transcript’s sequence. The efficiency of an mRNA can be tuned by varying the ribosome binding sites controlling the recruitment of the ribosomes, or the codon usage establishing the speed of protein elongation. In this work we propose transcript length as a further key determinant of translation efficiency. Based on a physical model that considers the kinetics of ribosomes advancing on the mRNA and diffusing in its surrounding, as well as mRNA circularisation and ribosome drop-off, we explain how the transcript length may play a central role in establishing ribosome recruitment and the overall translation rate of an mRNA. According to our results, the proximity of the 3′ end to the ribosomal recruitment site of the mRNA could induce a feedback in the translation process that would favour the recycling of ribosomes. We also demonstrate how this process may be involved in shaping the experimental ribosome density-gene length dependence. Finally, we argue that cells could exploit this mechanism to adjust and balance the usage of its ribosomal resources.

Yeast eIF4A enhances recruitment of mRNAs regardless of their structural complexity

eIF4A is a DEAD-box RNA-dependent ATPase thought to unwind RNA secondary structure in the 5'-untranslated regions (UTRs) of mRNAs to promote their recruitment to the eukaryotic translation pre-initiation complex (PIC). We show that eIF4A's ATPase activity is markedly stimulated in the presence of the PIC, independently of eIF4E•eIF4G, but dependent on subunits i and g of the heteromeric eIF3 complex. Surprisingly, eIF4A accelerated the rate of recruitment of all mRNAs tested, regardless of their degree of structural complexity. Structures in the 5'-UTR and 3' of the start codon synergistically inhibit mRNA recruitment in a manner relieved by eIF4A, indicating that the factor does not act solely to melt hairpins in 5'-UTRs. Our findings that eIF4A functionally interacts with the PIC and plays important roles beyond unwinding 5'-UTR structure is consistent with a recent proposal that eIF4A modulates the conformation of the 40S ribosomal subunit to promote mRNA recruitment.

The Stress Granule Transcriptome Reveals Principles of mRNA Accumulation in Stress Granules

Stress granules are mRNA-protein assemblies formed from nontranslating mRNAs. Stress granules are important in the stress response and may contribute to some degenerative diseases. Here, we describe the stress granule transcriptome of yeast and mammalian cells through RNA-sequencing (RNA-seq) analysis of purified stress granule cores and single-molecule fluorescence in situ hybridization (smFISH) validation. While essentially every mRNA, and some noncoding RNAs (ncRNAs), can be targeted to stress granules, the targeting efficiency varies from <1% to >95%. mRNA accumulation in stress granules correlates with longer coding and UTR regions and poor translatability. Quantifying the RNA-seq analysis by smFISH reveals that only 10% of bulk mRNA molecules accumulate in mammalian stress granules and that only 185 genes have more than 50% of their mRNA molecules in stress granules. These results suggest that stress granules may not represent a specific biological program of messenger ribonucleoprotein (mRNP) assembly, but instead form by condensation of nontranslating mRNPs in proportion to their length and lack of association with ribosomes.

Dynamic changes in eIF4F-mRNA interactions revealed by global analyses of environmental stress responses

Background

Translation factors eIF4E and eIF4G form eIF4F, which interacts with the messenger RNA (mRNA) 5′ cap to promote ribosome recruitment and translation initiation. Variations in the association of eIF4F with individual mRNAs likely contribute to differences in translation initiation frequencies between mRNAs. As translation initiation is globally reprogrammed by environmental stresses, we were interested in determining whether eIF4F interactions with individual mRNAs are reprogrammed and how this may contribute to global environmental stress responses.

Results

Using a tagged-factor protein capture and RNA-sequencing (RNA-seq) approach, we have assessed how mRNA associations with eIF4E, eIF4G1 and eIF4G2 change globally in response to three defined stresses that each cause a rapid attenuation of protein synthesis: oxidative stress induced by hydrogen peroxide and nutrient stresses caused by amino acid or glucose withdrawal. We find that acute stress leads to dynamic and unexpected changes in eIF4F–mRNA interactions that are shared among each factor and across the stresses imposed. eIF4F–mRNA interactions stabilised by stress are predominantly associated with translational repression, while more actively initiating mRNAs become relatively depleted for eIF4F. Simultaneously, other mRNAs are insulated from these stress-induced changes in eIF4F association.

Conclusion

Dynamic eIF4F–mRNA interaction changes are part of a coordinated early translational control response shared across environmental stresses. Our data are compatible with a model where multiple mRNA closed-loop complexes form with differing stability. Hence, unexpectedly, in the absence of other stabilising factors, rapid translation initiation on mRNAs correlates with less stable eIF4F interactions.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-017-1338-4) contains supplementary material, which is available to authorized users.

Stem-loop RNA labeling can affect nuclear and cytoplasmic mRNA processing

The binding of sequence-specific RNA-interacting proteins, such as the bacteriophage MS2 or PP7 coat proteins, to their corresponding target sequences has been extremely useful and widely used to visualize single mRNAs in vivo. However, introduction of MS2 stem-loops into yeast mRNAs has recently been shown to lead to the accumulation of RNA fragments, suggesting that the loops impair mRNA decay. This result was questioned, because fragment occurrence was mainly assessed using ensemble methods, and their cellular localization and its implications had not been addressed on a single transcript level. Here, we demonstrate that the introduction of either MS2 stem-loops (MS2SL) or PP7 stem-loops (PP7SL) can affect the processing and subcellular localization of mRNA. We use single-molecule fluorescence in situ hybridization (smFISH) to determine the localization of three independent mRNAs tagged with the stem-loop labeling systems in glucose-rich and glucose starvation conditions. Transcripts containing MS2SL or PP7SL display aberrant localization in both the nucleus and the cytoplasm. These defects are most prominent in glucose starvation conditions, with nuclear mRNA processing being altered and stem-loop fragments abnormally enriching in processing bodies (PBs). The mislocalization of SL-containing RNAs is independent of the presence of the MS2 or PP7 coat protein (MCP or PCP).

Codon optimality and mRNA decay

Recent evidence indicates that codon optimality is a broad determinant of mRNA stability. A study by Radhakrishnan et al. in Cell raises the possibility that the conserved DEAD-box protein Dhh1 underlies the phenomenon.

Predictive biophysical modeling and understanding of the dynamics of mRNA translation and its evolution

mRNA translation is the fundamental process of decoding the information encoded in mRNA molecules by the ribosome for the synthesis of proteins. The centrality of this process in various biomedical disciplines such as cell biology, evolution and biotechnology, encouraged the development of dozens of mathematical and computational models of translation in recent years. These models aimed at capturing various biophysical aspects of the process. The objective of this review is to survey these models, focusing on those based and/or validated on real large-scale genomic data. We consider aspects such as the complexity of the models, the biophysical aspects they regard and the predictions they may provide. Furthermore, we survey the central systems biology discoveries reported on their basis. This review demonstrates the fundamental advantages of employing computational biophysical translation models in general, and discusses the relative advantages of the different approaches and the challenges in the field.

Identification of the mRNA targets of tRNA-specific regulation using genome-wide simulation of translation

tRNA gene copy number is a primary determinant of tRNA abundance and therefore the rate at which each tRNA delivers amino acids to the ribosome during translation. Low-abundance tRNAs decode rare codons slowly, but it is unclear which genes might be subject to tRNA-mediated regulation of expression. Here, those mRNA targets were identified via global simulation of translation. In-silico mRNA translation rates were compared for each mRNA in both wild-type and a \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\rm{tRNA}}_{{\rm{CUG}}}^{{\rm{Gln}}}$\end{document}sup70-65 mutant, which exhibits a pseudohyphal growth phenotype and a 75% slower CAG codon translation rate. Of 4900 CAG-containing mRNAs, 300 showed significantly reduced in silico translation rates in a simulated tRNA mutant. Quantitative immunoassay confirmed that the reduced translation rates of sensitive mRNAs were \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\rm{tRNA}}_{{\rm{CUG}}}^{{\rm{Gln}}}$\end{document} concentration-dependent. Translation simulations showed that reduced \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\rm{tRNA}}_{{\rm{CUG}}}^{{\rm{Gln}}}$\end{document} concentrations triggered ribosome queues, which dissipated at reduced translation initiation rates. To validate this prediction experimentally, constitutive gcn2 kinase mutants were used to reduce in vivo translation initiation rates. This repaired the relative translational rate defect of target mRNAs in the sup70-65 background, and ameliorated sup70-65 pseudohyphal growth phenotypes. We thus validate global simulation of translation as a new tool to identify mRNA targets of tRNA-specific gene regulation.

Redefining the Translational Status of 80S Monosomes

Fully assembled ribosomes exist in two populations: polysomes and monosomes. While the former has been studied extensively, to what extent translation occurs on monosomes and its importance for overall translational output remain controversial. Here, we used ribosome profiling to examine the translational status of 80S monosomes in Saccharomyces cerevisiae. We found that the vast majority of 80S monosomes are elongating, not initiating. Further, most mRNAs exhibit some degree of monosome occupancy, with monosomes predominating on nonsense-mediated decay (NMD) targets, upstream open reading frames (uORFs), canonical ORFs shorter than ∼ 590 nt, and ORFs for which the total time required to complete elongation is substantially shorter than that required for initiation. Importantly, mRNAs encoding low-abundance regulatory proteins tend to be enriched in the monosome fraction. Our data highlight the importance of monosomes for the translation of highly regulated mRNAs.

The molecular choreography of protein synthesis: translational control, regulation, and pathways

Translation of proteins by the ribosome regulates gene expression, with recent results underscoring the importance of translational control. Misregulation of translation underlies many diseases, including cancer and many genetic diseases. Decades of biochemical and structural studies have delineated many of the mechanistic details in prokaryotic translation, and sketched the outlines of eukaryotic translation. However, translation may not proceed linearly through a single mechanistic pathway, but likely involves multiple pathways and branchpoints. The stochastic nature of biological processes would allow different pathways to occur during translation that are biased by the interaction of the ribosome with other translation factors, with many of the steps kinetically controlled. These multiple pathways and branchpoints are potential regulatory nexus, allowing gene expression to be tuned at the translational level. As research focus shifts toward eukaryotic translation, certain themes will be echoed from studies on prokaryotic translation. This review provides a general overview of the dynamic data related to prokaryotic and eukaryotic translation, in particular recent findings with single-molecule methods, complemented by biochemical, kinetic, and structural findings. We will underscore the importance of viewing the process through the viewpoints of regulation, translational control, and heterogeneous pathways.

Rapidly Translated Polypeptides Are Preferred Substrates for Cotranslational Protein Degradation

Nascent polypeptides are degraded by the proteasome concurrently with their synthesis on the ribosome. This process, called cotranslational protein degradation (CTPD), has been observed for years, but the underlying mechanisms remain poorly understood. Equally unclear are the identities of cellular proteins genuinely subjected to CTPD. Here we report the identification of CTPD substrates in the yeast Saccharomyces cerevisiae via a quantitative proteomic analysis. We compared the abundance of individual ribosome-bound nascent chains between a wild type strain and a mutant defective in CTPD. Of 1,422 proteins acquired from the proteomic analysis, 289 species are efficient CTPD substrates, with >30% of their nascent chains degraded cotranslationally. We found that proteins involved in translation, ribosome biogenesis, nuclear transport, and amino acid metabolism are more likely to be targeted for CTPD. There is a strong correlation between CTPD and the translation efficiency. CTPD occurs preferentially to rapidly translated polypeptides. CTPD is also influenced by the protein sequence length; longer polypeptides are more susceptible to CTPD. In addition, proteins with N-terminal disorder have a higher probability of being degraded cotranslationally. Interestingly, the CTPD efficiency is not related to the half-lives of mature proteins. These results for the first time indicate an inverse correlation between CTPD and cotranslational folding on a proteome scale. The implications of this study with respect to the physiological significance of CTPD are discussed.

Evolutionary Pressures on the Yeast Transcriptome

Codon usage bias (CUB) is the well known phenomenon that the frequency of synonymous codons is unequal. This is presumably the result of adaptive pressures favouring some codons over others. The underlying reason for this pressure is unknown, although a large number of possible driver mechanisms have been proposed; one of them is the decoding time. The standard model to calculate decoding time is the Gromadski-Rodnina model. Yet, recently, there have been a number of studies arguing to the effect that this conventional speed-model is not relevant to understand the dynamics of translation. However, results remain inconclusive so far. This contribution takes a novel approach to address this issue based on comparing mRNA with random synonymous variants to estimate the evolutionary pressures that have acted on the transcriptome. It emerges that over 70 percent of ORFs have been subject to a strong selection pressure for translation speed and that there is also a strong selection pressure for the avoidance of traffic jams. Finally, it is also shown that both homogeneous and very heterogeneous transcripts are over-represented. These results corroborate the validity of the Gromadski-Rodnina model.

Unbiased Quantitative Models of Protein Translation Derived from Ribosome Profiling Data

Translation of RNA to protein is a core process for any living organism. While for some steps of this process the effect on protein production is understood, a holistic understanding of translation still remains elusive. In silico modelling is a promising approach for elucidating the process of protein synthesis. Although a number of computational models of the process have been proposed, their application is limited by the assumptions they make. Ribosome profiling (RP), a relatively new sequencing-based technique capable of recording snapshots of the locations of actively translating ribosomes, is a promising source of information for deriving unbiased data-driven translation models. However, quantitative analysis of RP data is challenging due to high measurement variance and the inability to discriminate between the number of ribosomes measured on a gene and their speed of translation. We propose a solution in the form of a novel multi-scale interpretation of RP data that allows for deriving models with translation dynamics extracted from the snapshots. We demonstrate the usefulness of this approach by simultaneously determining for the first time per-codon translation elongation and per-gene translation initiation rates of Saccharomyces cerevisiae from RP data for two versions of the Totally Asymmetric Exclusion Process (TASEP) model of translation. We do this in an unbiased fashion, by fitting the models using only RP data with a novel optimization scheme based on Monte Carlo simulation to keep the problem tractable. The fitted models match the data significantly better than existing models and their predictions show better agreement with several independent protein abundance datasets than existing models. Results additionally indicate that the tRNA pool adaptation hypothesis is incomplete, with evidence suggesting that tRNA post-transcriptional modifications and codon context may play a role in determining codon elongation rates.

Translation, the process of synthesizing proteins from mRNA templates, is an essential biological process in all living organisms. A better understanding of this process will have ramifications in various fields—from gene regulation, disease understanding and medicine to biotechnology and synthetic biology. Nonetheless, a holistic understanding of the processes remains elusive, making computational modelling a promising approach for studying it. However, accurate modelling of translation is challenging due to many assumptions made by such models and due to the sheer number of parameters that need to be specified. Here, we propose to fit models of translation onto ribosome profiling measurements, which record snapshots of the locations of actively translating ribosomes on mRNAs from millions of cells. We develop statistical and computational methods for fitting the Totally Asymmetric Exclusion Process (TASEP) models of translation on these measurements and verify them by deriving highly accurate translation models for the baker’s yeast Saccharomyces cerevisiae, which outperform existing models on independent datasets. We find that fitted elongation rate parameters from the derived models deviate significantly from the widely accepted tRNA pool adaptation hypothesis.

Noise analysis of genome-scale protein synthesis using a discrete computational model of translation

Noise in genetic networks has been the subject of extensive experimental and computational studies. However, very few of these studies have considered noise properties using mechanistic models that account for the discrete movement of ribosomes and RNA polymerases along their corresponding templates (messenger RNA (mRNA) and DNA). The large size of these systems, which scales with the number of genes, mRNA copies, codons per mRNA, and ribosomes, is responsible for some of the challenges. Additionally, one should be able to describe the dynamics of ribosome exchange between the free ribosome pool and those bound to mRNAs, as well as how mRNA species compete for ribosomes. We developed an efficient algorithm for stochastic simulations that addresses these issues and used it to study the contribution and trade-offs of noise to translation properties (rates, time delays, and rate-limiting steps). The algorithm scales linearly with the number of mRNA copies, which allowed us to study the importance of genome-scale competition between mRNAs for the same ribosomes. We determined that noise is minimized under conditions maximizing the specific synthesis rate. Moreover, sensitivity analysis of the stochastic system revealed the importance of the elongation rate in the resultant noise, whereas the translation initiation rate constant was more closely related to the average protein synthesis rate. We observed significant differences between our results and the noise properties of the most commonly used translation models. Overall, our studies demonstrate that the use of full mechanistic models is essential for the study of noise in translation and transcription.

Accounting for experimental noise reveals that mRNA levels, amplified by post-transcriptional processes, largely determine steady-state protein levels in yeast

Cells respond to their environment by modulating protein levels through mRNA transcription and post-transcriptional control. Modest observed correlations between global steady-state mRNA and protein measurements have been interpreted as evidence that mRNA levels determine roughly 40% of the variation in protein levels, indicating dominant post-transcriptional effects. However, the techniques underlying these conclusions, such as correlation and regression, yield biased results when data are noisy, missing systematically, and collinear---properties of mRNA and protein measurements---which motivated us to revisit this subject. Noise-robust analyses of 24 studies of budding yeast reveal that mRNA levels explain more than 85% of the variation in steady-state protein levels. Protein levels are not proportional to mRNA levels, but rise much more rapidly. Regulation of translation suffices to explain this nonlinear effect, revealing post-transcriptional amplification of, rather than competition with, transcriptional signals. These results substantially revise widely credited models of protein-level regulation, and introduce multiple noise-aware approaches essential for proper analysis of many biological phenomena.

Accounting for experimental noise reveals that mRNA levels, amplified by post-transcriptional processes, largely determine steady-state protein levels in yeast

Cells respond to their environment by modulating protein levels through mRNA transcription and post-transcriptional control. Modest observed correlations between global steady-state mRNA and protein measurements have been interpreted as evidence that mRNA levels determine roughly 40% of the variation in protein levels, indicating dominant post-transcriptional effects. However, the techniques underlying these conclusions, such as correlation and regression, yield biased results when data are noisy, missing systematically, and collinear---properties of mRNA and protein measurements---which motivated us to revisit this subject. Noise-robust analyses of 24 studies of budding yeast reveal that mRNA levels explain more than 85% of the variation in steady-state protein levels. Protein levels are not proportional to mRNA levels, but rise much more rapidly. Regulation of translation suffices to explain this nonlinear effect, revealing post-transcriptional amplification of, rather than competition with, transcriptional signals. These results substantially revise widely credited models of protein-level regulation, and introduce multiple noise-aware approaches essential for proper analysis of many biological phenomena.

Co-regulation of translation in protein complexes

Background

Co-regulation of gene expression has been known for many years, and studied widely both globally and for individual genes. Nevertheless, most analyses concerned transcriptional control, which in case of physically interacting proteins and protein complex subunits may be of secondary importance. This research is the first quantitative analysis that provides global-scale evidence for translation co-regulation among associated proteins.

Results

By analyzing the results of our previous quantitative model of translation, we have demonstrated that protein production rates plus several other translational parameters, such as mRNA and protein abundance, or number of produced proteins from a gene, are well concerted between stable complex subunits and party hubs. This may be energetically favorable during synthesis of complex building blocks and ensure their accurate production in time. In contrast, for connections with regulatory particles and date hubs translational co-regulation is less visible, indicating that in these cases maintenance of accurate levels of interacting particles is not necessarily beneficial.

Conclusions

Similar results obtained for distantly related model organisms, Saccharomyces cerevisiae and Homo sapiens, suggest that the phenomenon of translational co-regulation applies to the variety of living organisms and concerns many complex constituents. This phenomenon was also observed among the set of functionally linked proteins from Escherichia coli operons. This leads to the conclusion that translational regulation of a protein should always be studied with respect to the expression of its primary interacting partners.

Reviewers

This article was reviewed by Sandor Pongor and Claus Wilke.

Electronic supplementary material

The online version of this article (doi:10.1186/s13062-015-0048-7) contains supplementary material, which is available to authorized users.

Translate to divide: сontrol of the cell cycle by protein synthesis

Protein synthesis underpins much of cell growth and, consequently, cell multiplication. Understanding how proliferating cells commit and progress into the cell cycle requires knowing not only which proteins need to be synthesized, but also what determines their rate of synthesis during cell division.

Transcriptional program for nitrogen starvation-induced lipid accumulation in Chlamydomonas reinhardtii

BACKGROUND

Algae accumulate lipids to endure different kinds of environmental stresses including macronutrient starvation. Although this response has been extensively studied, an in depth understanding of the transcriptional regulatory network (TRN) that controls the transition into lipid accumulation remains elusive. In this study, we used a systems biology approach to elucidate the transcriptional program that coordinates the nitrogen starvation-induced metabolic readjustments that drive lipid accumulation in Chlamydomonas reinhardtii.

RESULTS

We demonstrate that nitrogen starvation triggered differential regulation of 2147 transcripts, which were co-regulated in 215 distinct modules and temporally ordered as 31 transcriptional waves. An early-stage response was triggered within 12 min that initiated growth arrest through activation of key signaling pathways, while simultaneously preparing the intracellular environment for later stages by modulating transport processes and ubiquitin-mediated protein degradation. Subsequently, central metabolism and carbon fixation were remodeled to trigger the accumulation of triacylglycerols. Further analysis revealed that these waves of genome-wide transcriptional events were coordinated by a regulatory program orchestrated by at least 17 transcriptional regulators, many of which had not been previously implicated in this process. We demonstrate that the TRN coordinates transcriptional downregulation of 57 metabolic enzymes across a period of nearly 4 h to drive an increase in lipid content per unit biomass. Notably, this TRN appears to also drive lipid accumulation during sulfur starvation, while phosphorus starvation induces a different regulatory program. The TRN model described here is available as a community-wide web-resource at http://networks.systemsbiology.net/chlamy-portal.

CONCLUSIONS

In this work, we have uncovered a comprehensive mechanistic model of the TRN controlling the transition from N starvation to lipid accumulation. The program coordinates sequentially ordered transcriptional waves that simultaneously arrest growth and lead to lipid accumulation. This study has generated predictive tools that will aid in devising strategies for the rational manipulation of regulatory and metabolic networks for better biofuel and biomass production.

Evaluating the fitness cost of protein expression in Saccharomyces cerevisiae

Protein metabolism is one of the most costly processes in the cell and is therefore expected to be under the effective control of natural selection. We stimulated yeast strains to overexpress each single gene product to approximately 1% of the total protein content. Consistent with previous reports, we found that excessive expression of proteins containing disordered or membrane-protruding regions resulted in an especially high fitness cost. We estimated these costs to be nearly twice as high as for other proteins. There was a ten-fold difference in cost if, instead of entire proteins, only the disordered or membrane-embedded regions were compared with other segments. Although the cost of processing bulk protein was measurable, it could not be explained by several tested protein features, including those linked to translational efficiency or intensity of physical interactions after maturation. It most likely included a number of individually indiscernible effects arising during protein synthesis, maturation, maintenance, (mal)functioning, and disposal. When scaled to the levels normally achieved by proteins in the cell, the fitness cost of dealing with one amino acid in a standard protein appears to be generally very low. Many single amino acid additions or deletions are likely to be neutral even if the effective population size is as large as that of the budding yeast. This should also apply to substitutions. Selection is much more likely to operate if point mutations affect protein structure by, for example, extending or creating stretches that tend to unfold or interact improperly with membranes.

Accounting for biases in riboprofiling data indicates a major role for proline in stalling translation

The recent advent of ribosome profiling-sequencing of short ribosome-bound fragments of mRNA-has offered an unprecedented opportunity to interrogate the sequence features responsible for modulating translational rates. Nevertheless, numerous analyses of the first riboprofiling data set have produced equivocal and often incompatible results. Here we analyze three independent yeast riboprofiling data sets, including two with much higher coverage than previously available, and find that all three show substantial technical sequence biases that confound interpretations of ribosomal occupancy. After accounting for these biases, we find no effect of previously implicated factors on ribosomal pausing. Rather, we find that incorporation of proline, whose unique side-chain stalls peptide synthesis in vitro, also slows the ribosome in vivo. We also reanalyze a method that implicated positively charged amino acids as the major determinant of ribosomal stalling and demonstrate that it produces false signals of stalling in low-coverage data. Our results suggest that any analysis of riboprofiling data should account for sequencing biases and sparse coverage. To this end, we establish a robust methodology that enables analysis of ribosome profiling data without prior assumptions regarding which positions spanned by the ribosome cause stalling.

Dual-targeted proteins tend to be more evolutionarily conserved

In eukaryotic cells, identical proteins can be located in more than a single subcellular compartment, a phenomenon termed dual targeting. We hypothesized that dual-targeted proteins should be more evolutionary conserved than exclusive mitochondrial proteins, due to separate selective pressures administered by the different compartments to maintain the functions associated with the protein sequences. We employed codon usage bias, propensity for gene loss, phylogenetic relationships, conservation analysis at the DNA level, and gene expression, to test our hypothesis. Our findings indicate that, indeed, dual-targeted proteins are significantly more conserved than their exclusively targeted counterparts. We then used this trait of gene conservation, together with previously identified traits of dual-targeted proteins (such as protein net charge and mitochondrial targeting sequence strength) to 1) create, for the first time (due to addition of conservation parameters), a tool for the prediction of dual-targeted mitochondrial proteins based on protein and mRNA sequences, and 2) show that molecular mechanisms involving one versus two translation products are not correlated with specific dual-targeting parameters. Finally, we discuss what evolutionary pressure maintains protein dual targeting in eukaryotes and deduce, as we initially hypothesized, that it is the discrete functions of these proteins in the different subcellular compartments, regardless of their dual-targeting mechanism.