Explaining complex codon usage patterns with selection for translational efficiency, mutation bias, and genetic drift

Rate-limiting steps in yeast protein translation

Deep sequencing now provides detailed snapshots of ribosome occupancy on mRNAs. We leverage these data to parameterize a computational model of translation, keeping track of every ribosome, tRNA, and mRNA molecule in a yeast cell. We determine the parameter regimes in which fast initiation or high codon bias in a transgene increases protein yield and infer the initiation rates of endogenous Saccharomyces cerevisiae genes, which vary by several orders of magnitude and correlate with 5′ mRNA folding energies. Our model recapitulates the previously reported 5′-to-3′ ramp of decreasing ribosome densities, although our analysis shows that this ramp is caused by rapid initiation of short genes rather than slow codons at the start of transcripts. We conclude that protein production in healthy yeast cells is typically limited by the availability of free ribosomes, whereas protein production under periods of stress can sometimes be rescued by reducing initiation or elongation rates.

•

Computational model of translation tracks all ribosomes, tRNAs, and mRNAs in a cell

•

Translation is generally limited by initiation, not elongation

•

Model allows inference of initiation rates for all yeast genes

•

Ramp of 5′ ribosomes is caused by rapid initiation of short genes

Efficient translation initiation dictates codon usage at gene start

Rare codons are enriched at gene start in many genomes. Genome analysis and experimental testing show that this enrichment evolved to keep the ribosome binding site free from stable mRNA structures, in order to facilitate efficient translation initiation.

The use of rare codons coincides with suppression of mRNA structures at the ribosome binding site across genomes.

There is preferential selection for synonymous codons that reduce GC-content at the beginning of genes and a stronger pressure for rare codon usage in GC-rich organisms.

Amino acids encoded by AU-rich codons are preferred at gene start.

Experimental results show that mRNA structure at translation start strongly influences protein yield.

The genetic code is degenerate; thus, protein evolution does not uniquely determine the coding sequence. One of the puzzles in evolutionary genetics is therefore to uncover evolutionary driving forces that result in specific codon choice. In many bacteria, the first 5–10 codons of protein-coding genes are often codons that are less frequently used in the rest of the genome, an effect that has been argued to arise from selection for slowed early elongation to reduce ribosome traffic jams. However, genome analysis across many species has demonstrated that the region shows reduced mRNA folding consistent with pressure for efficient translation initiation. This raises the possibility that unusual codon usage is a side effect of selection for reduced mRNA structure. Here we discriminate between these two competing hypotheses, and show that in bacteria selection favours codons that reduce mRNA folding around the translation start, regardless of whether these codons are frequent or rare. Experiments confirm that primarily mRNA structure, and not codon usage, at the beginning of genes determines the translation rate.

Estimating selection on synonymous codon usage from noisy experimental data

A key goal in molecular evolution is to extract mechanistic insights from signatures of selection. A case study is codon usage, where despite many recent advances and hypotheses, two longstanding problems remain: the relative contribution of selection and mutation in determining codon frequencies and the relative contribution of translational speed and accuracy to selection. The relevant targets of selection--the rate of translation and of mistranslation of a codon per unit time in the cell--can only be related to mechanistic properties of the translational apparatus if the number of transcripts per cell is known, requiring use of gene expression measurements. Perhaps surprisingly, different gene-expression data sets yield markedly different estimates of selection. We show that this is largely due to measurement noise, notably due to differences between studies rather than instrument error or biological variability. We develop an analytical framework that explicitly models noise in expression in the context of the population-genetic model. Estimates of mutation and selection strength in budding yeast produced by this method are robust to the expression data set used and are substantially higher than estimates using a noise-blind approach. We introduce per-gene selection estimates that correlate well with previous scoring systems, such as the codon adaptation index, while now carrying an evolutionary interpretation. On average, selection for codon usage in budding yeast is weak, yet our estimates show that genes range from virtually unselected to average per-codon selection coefficients above the inverse population size. Our analytical framework may be generally useful for distinguishing biological signals from measurement noise in other applications that depend upon measurements of gene expression.

Translational signatures and mRNA levels are highly correlated in human stably expressed genes

Background

Gene expression is one of the most relevant biological processes of living cells. Due to the relative small population sizes, it is predicted that human gene sequences are not strongly influenced by selection towards expression efficiency. One of the major problems in estimating to what extent gene characteristics can be selected to maximize expression efficiency is the wide variation that exists in RNA and protein levels among physiological states and different tissues. Analyses of datasets of stably expressed genes (i.e. with consistent expression between physiological states and tissues) would provide more accurate and reliable measurements of associations between variations of a specific gene characteristic and expression, and how distinct gene features work to optimize gene expression.

Results

Using a dataset of human genes with consistent expression between physiological states we selected gene sequence signatures related to translation that can predict about 42% of mRNA variation. The prediction can be increased to 51% when selecting genes that are stably expressed in more than 1 tissue. These genes are enriched for translation and ribosome biosynthesis processes and have higher translation efficiency scores, smaller coding sequences and 3^′ UTR sizes and lower folding energies when compared to other datasets. Additionally, the amino acid frequencies weighted by expression showed higher correlations with isoacceptor tRNA gene copy number, and smaller absolute correlation values with biosynthetic costs.

Conclusion

Our results indicate that human gene sequence characteristics related to transcription and translation processes can co-evolve in an integrated manner in order to optimize gene expression.

Weak 5'-mRNA secondary structures in short eukaryotic genes

Experimental studies of translation have found that short genes tend to exhibit greater densities of ribosomes than long genes in eukaryotic species. It remains an open question whether the elevated ribosome density on short genes is due to faster initiation or slower elongation dynamics. Here, we address this question computationally using 5′-mRNA folding energy as a proxy for translation initiation rates and codon bias as a proxy for elongation rates. We report a significant trend toward reduced 5′-secondary structure in shorter coding sequences, suggesting that short genes initiate faster during translation. We also find a trend toward higher 5′-codon bias in short genes, suggesting that short genes elongate faster than long genes. Both of these trends hold across a diverse set of eukaryotic taxa. Thus, the elevated ribosome density on short eukaryotic genes is likely caused by differential rates of initiation, rather than differential rates of elongation.

Non-optimal codon usage is a mechanism to achieve circadian clock conditionality

Circadian rhythms are oscillations in biological processes that function as a key adaptation to the daily rhythms of most environments. In the model cyanobacterial circadian clock system, the core oscillator proteins are encoded by the gene cluster kaiABC¹. Genes with high expression and functional importance like the kai genes are usually encoded by optimal codons, yet the codon usage bias of the kaiBC genes is not optimized for translational efficiency. We discovered a relationship between codon usage and a general property of circadian rhythms called conditionality; namely, that endogenous rhythmicity is robustly expressed under some environmental conditions but not under others². Despite the generality of circadian conditionality, however, its molecular basis is unknown for any system. Here we show that non-optimal codon usage was selected as a post-transcriptional mechanism to switch between circadian and non-circadian regulation of gene expression as an adaptive response to environmental conditions. When the kaiBC sequence was experimentally optimized to enhance expression of the KaiB and KaiC proteins, intrinsic rhythmicity was enhanced at cool temperatures that are experienced by this organism in its natural habitat. However, fitness at those temperatures was highest in cells whose endogenous rhythms were suppressed at cool temperatures as compared with cells exhibiting high-amplitude rhythmicity. These results indicate natural selection against circadian systems in cyanobacteria that are intrinsically robust at cool temperatures. Modulation of circadian amplitude is therefore critical to its adaptive significance³. Moreover, these results show the direct effects of codon usage on a complex phenotype and organismal fitness. Our work also challenges the long-standing view of directional selection towards optimal codons^4–7, and provides a key example of natural selection against optimal codon to achieve adaptive responses to environmental changes.

Evolutionary conservation of codon optimality reveals hidden signatures of cotranslational folding

The choice of codons can influence local translation kinetics during protein synthesis. Whether codon preference is linked to co-translational regulation of polypeptide folding remains unclear. Here, we derive a revised translational efficiency scale that incorporates the competition between tRNA supply and demand. Applying this scale to ten closely related yeasts, we uncover the evolutionary conservation of codon optimality in eukaryotes. This analysis reveals universal patterns of conserved optimal and nonoptimal codons, often in clusters, which associate with the secondary structure of the translated polypeptides independent of the levels of expression. Our analysis suggests an evolved function for codon optimality in regulating the rhythm of elongation to facilitate co-translational polypeptide folding, beyond its previously proposed role of adapting to the cost of expression. These findings establish how mRNA sequences are generally under selection to optimize the co-translational folding of corresponding polypeptides.

Vaccinia and influenza A viruses select rather than adjust tRNAs to optimize translation

Transfer RNAs (tRNAs) are central to protein synthesis and impact translational speed and fidelity by their abundance. Here we examine the extent to which viruses manipulate tRNA populations to favor translation of their own genes. We study two very different viruses: influenza A virus (IAV), a medium-sized (13 kB genome) RNA virus; and vaccinia virus (VV), a large (200 kB genome) DNA virus. We show that the total cellular tRNA population remains unchanged following viral infection, whereas the polysome-associated tRNA population changes dramatically in a virus-specific manner. The changes in polysome-associated tRNA levels reflect the codon usage of viral genes, suggesting the existence of local tRNA pools optimized for viral translation.

Comparative genome analysis of six malarial parasites using codon usage bias based tools

Codon usage bias (CUB) is an omnipresent phenomenon, which occurs in nearly all organisms. Previous studies of codon bias in Plasmodium species were based on a limited dataset. This study uses whole genome datasets for comparative genome analysis of six Plasmodium species using CUB and other related methods for the first time. Codon usage bias, compositional variation in translated amino acid frequency, effective number of codons and optimal codons are analyzed for P.falciparum, P.vivax, P.knowlesi, P.berghei, P.chabaudii and P.yoelli. A plot of effective number of codons versus GC3 shows their differential codon usage pattern arises due to a combination of mutational and translational selection pressure. The increased relative usage of adenine and thymine ending optimal codons in highly expressed genes of P.falciparum is the result of higher composition biased pressure, and usage of guanine and cytosine bases at third codon position can be explained by translational selection pressure acting on them. While higher usage of adenine and thymine bases at third codon position in optimal codons of P.vivax highlights the role of translational selection pressure apart from composition biased mutation pressure in shaping their codon usage pattern. The frequency of those amino acids that are encoded by AT ending codons are significantly high in P.falciparum due to action of high composition biased mutational pressure compared with other Plasmodium species. The CUB variation in the three rodent parasites, P.berghei, P.chabaudii and P.yoelli is strikingly similar to that of P.falciparum. The simian and human malarial parasite, P.knowlesi shows a variation in codon usage bias similar to P.vivax but on closer study there are differences confirmed by the method of Principal Component Analysis (PCA).

ABBREVIATIONS

CDS - Coding sequences, GC1 - GC composition at first site of codon, GC2 - GC composition at second site of codon, GC3 - GC composition at third site of codon, Ala - Alanine, Arg - Arginine, Asn - Asparagine, Asp - Aspartic acid, Cys - Cysteine, Gln - Glutamine Glu - Glutamic acid Gly - Glycine His - Histidine Ile - Isoleucine Leu - Leucine Lys - Lysine Met - Methionine Phe - Phenylalanine Pro - Proline Ser - Serine Thr - Threonine Trp - Tryptophan Tyr - Tyrosine Val - Valine.

On ribosome load, codon bias and protein abundance

Different codons encoding the same amino acid are not used equally in protein-coding sequences. In bacteria, there is a bias towards codons with high translation rates. This bias is most pronounced in highly expressed proteins, but a recent study of synthetic GFP-coding sequences did not find a correlation between codon usage and GFP expression, suggesting that such correlation in natural sequences is not a simple property of translational mechanisms. Here, we investigate the effect of evolutionary forces on codon usage. The relation between codon bias and protein abundance is quantitatively analyzed based on the hypothesis that codon bias evolved to ensure the efficient usage of ribosomes, a precious commodity for fast growing cells. An explicit fitness landscape is formulated based on bacterial growth laws to relate protein abundance and ribosomal load. The model leads to a quantitative relation between codon bias and protein abundance, which accounts for a substantial part of the observed bias for E. coli. Moreover, by providing an evolutionary link, the ribosome load model resolves the apparent conflict between the observed relation of protein abundance and codon bias in natural sequences and the lack of such dependence in a synthetic gfp library. Finally, we show that the relation between codon usage and protein abundance can be used to predict protein abundance from genomic sequence data alone without adjustable parameters.

Determinants of translation elongation speed and ribosomal profiling biases in mouse embryonic stem cells

Ribosomal profiling is a promising approach with increasing popularity for studying translation. This approach enables monitoring the ribosomal density along genes at a resolution of single nucleotides.

In this study, we focused on ribosomal density profiles of mouse embryonic stem cells. Our analysis suggests, for the first time, that even in mammals such as M. musculus the elongation speed is significantly and directly affected by determinants of the coding sequence such as: 1) the adaptation of codons to the tRNA pool; 2) the local mRNA folding of the coding sequence; 3) the local charge of amino acids encoded in the codon sequence. In addition, our analyses suggest that in general, the translation velocity of ribosomes is slower at the beginning of the coding sequence and tends to increase downstream.

Finally, a comparison of these data to the expected biophysical behavior of translation suggests that it suffers from some unknown biases. Specifically, the ribosomal flux measured on the experimental data increases along the coding sequence; however, according to any biophysical model of ribosomal movement lacking internal initiation sites, the flux is expected to remain constant or decrease. Thus, developing experimental and/or statistical methods for understanding, detecting and dealing with such biases is of high importance.

Gene translation is the process by which ribosomes translate mRNA molecules to proteins, a central process in all living organisms. Thus, understanding the biophysics of gene translation and the way its efficiency is encoded in the different features of the coding sequence has ramifications to every biomedical discipline. Recently, a new large-scale experimental approach named ‘ribosomal profiling’, has been developed for monitoring the ribosomal density at a resolution of single nucleotides. In this study, we analyzed ribosomal profiling data of mouse embryonic stem cells. These data enabled us to directly show that translation velocity is affected by the adaptation of codons to the tRNA pool, local mRNA folding of coding sequence, and local charge of the amino acids encoded in the coding sequence. In addition, our analyses suggest that ribosomal speed tends to be slower at the beginning of the coding sequence. Finally, we report possible biases in the ‘ribosomal profiling’ procedure that should be considered in future studies utilizing this method.

On the steady-state distribution in the homogeneous ribosome flow model

A central biological process in all living organisms is gene translation. Developing a deeper understanding of this complex process may have ramifications to almost every biomedical discipline. Reuveni et al. recently proposed a new computational model of gene translation called the Ribosome Flow Model (RFM). In this paper, we consider a particular case of this model, called the Homogeneous Ribosome Flow Model (HRFM). From a biological viewpoint, this corresponds to the case where the transition rates of all the coding sequence codons are identical. This regime has been suggested recently based on experiments in mouse embryonic cells. We consider the steady-state distribution of the HRFM. We provide formulas that relate the different parameters of the model in steady state. We prove the following properties: 1) the ribosomal density profile is monotonically decreasing along the coding sequence; 2) the ribosomal density at each codon monotonically increases with the initiation rate; and 3) for a constant initiation rate, the translation rate monotonically decreases with the length of the coding sequence. In addition, we analyze the translation rate of the HRFM at the limit of very high and very low initiation rate, and provide explicit formulas for the translation rate in these two cases. We discuss the relationship between these theoretical results and biological findings on the translation process.

Stability analysis of the ribosome flow model

Gene translation is a central process in all living organisms. Developing a better understanding of this complex process may have ramifications to almost every biomedical discipline. Recently, Reuveni et al. proposed a new computational model of this process called the ribosome flow model (RFM). In this study, we show that the dynamical behavior of the RFM is relatively simple. There exists a unique equilibrium point e and every trajectory converges to e. Furthermore, convergence is monotone in the sense that the distance to e can never increase. This qualitative behavior is maintained for any feasible set of parameter values, suggesting that the RFM is highly robust. Our analysis is based on a contraction principle and the theory of monotone dynamical systems. These analysis tools may prove useful in studying other properties of the RFM as well as additional intracellular biological processes.

The construction and expression of lysine-rich gene in the mammary gland of transgenic mice

Lysine is the limiting amino acid in cereal grains, which represent a major source of human food and animal feed worldwide, and is considered the most important of the essential amino acids. In this study, β-casein, αS2-casein, and lactotransferrin cDNA clone fragments encoding lysine-rich peptides were fused together to generate a lysine-rich (LR) gene and the mammary gland-specific expression vector pBC1-LR-NEO(r) was constructed. Transgenic mice were generated by pronuclear microinjection of the linearized expression vectors harboring the LR transgene. The transgenic mice and their offspring were examined using multiplex polymerase chain reaction (PCR), Southern blotting, reverse transcriptase-PCR, in situ hybridization, and Western blotting techniques. Our results showed that the LR gene was successfully integrated into the mouse genome and was transmitted stably. The specific LR gene expression was restricted to the mammary gland, active alveoli of the transgenic female mice during lactation. The lysine level of the two transgenic lines was significantly higher than that of nontransgenic controls (p<0.05). In addition, the growth performance of transgenic pups was enhanced by directly feeding them the LR protein-enriched transgenic milk. Our results demonstrated that lysine-rich gene was successfully constructed and expressed in mammary gland of transgenic mice. This study will provide a better understanding of how mammary gland expression systems that increase the lysine content of milk can be applied to other mammals, such as cows.

CBDB: the codon bias database

Background

In many genomes, a clear preference in the usage of particular codons exists. The mechanisms that induce codon biases remain an open question; studies have attributed codon usage to translational selection, mutational bias and drift. Furthermore, correlations between codon usage within host genomes and their viral pathogens have been observed for a myriad of host-virus systems. As such, numerous studies have investigated codon usage and codon bias in an effort to better understand how species evolve. Numerous metrics have been developed to identify biases in codon usage. In addition, a few data repositories of codon bias data are available, differing in the metrics reported as well as the number and taxonomy of strains examined.

Description

We have created a new web resource called the Codon Bias Database (CBDB) which provides information regarding the codon bias within the set of highly expressed genes for 300+ bacterial genomes. CBDB was developed to provide a resource for researchers investigating codon bias in bacteria, facilitating comparisons between strains and species. Furthermore, the site was created to serve those studying adaptation in phage; the genera selected for this first release of CBDB all have sequenced, annotated bacteriophages. The annotations and sequences for the highly expressed gene set are available for each strain in addition to the strain’s codon bias measurements.

Conclusions

Comparing species and strains provides a comprehensive look at how codon usage has been shaped over evolutionary time and can elucidate the putative mechanisms behind it. The Codon Bias Database provides a centralized repository of look-up tables and codon usage bias measures for a wide variety of genera, species and strains. Through our analysis of the variation in codon usage within the strains presently available, we find that most members of a genus have a codon composition most similar to other members of its genus, although not necessarily other members of its species.