Translationally optimal codons associate with structurally sensitive sites in proteins

Compositional Features and Codon Usage Pattern of Genes Associated with Parkinson's Disease

Codon usage bias (CUB) is the phenomenon of non-uniform usage of synonymous codons in which some codons are more used than others and it helps in understanding the molecular organization of genome. Bioinformatic approach was used to analyze the protein-coding sequences of genes associated with Parkinson's disease (PD) to explore compositional features and codon usage pattern as no details work was reported yet. The average improved effective number of codons (Nc) and Nc prime were 42.74 and 44.26 respectively, indicated that CUB was low in these genes. In most of the genes, the overall GC content was almost 50% and GC content at the 1st codon position was the highest while GC content at the 2nd codon position was lowest. Relative synonymous codon usage (RSCU) analysis elucidated over-represented (p > 1.6) and under-represented codons (p < 0.6). The GTG (Val) is the only codon over-represented in all genes. Over-represented codons except (GTG) were A or T ending while under-represented codons (except ACT) were G or C ending. The codons namely TTA (Leu), CTA (Leu), ATC (Ile), ATA (Ile), AGT (Ser), AAC (Asn), TGT (Cys), TGC (Cys), CGC (Arg), AGA (Arg), and AGG (Arg) were absent in SNCA1 to SNCA8 genes. The codon TCG (Ser) was absent in all genes except UCHL1 and PINK1. Correspondence analysis (COA) revealed that the pattern of codon usage differs among genes associated with PD. Neutrality plot analysis indicated some of the points are diagonal distribution suggested that mutation pressure influenced the CUB in genes associated with PD.

Differentially used codons among essential genes in bacteria identified by machine learning-based analysis

Codon usage bias (CUB), the uneven usage of synonymous codons encoding the same amino acid, differs among genes within and across bacteria genomes. CUB is known to be influenced by gene expression and accordingly, CUB differs between the high-expression and low-expression genes in several bacteria. In this article, we have extended codon usage study considering gene essentiality as a feature. Using machine learning (ML) based approaches, we have analysed Relative Synonymous Codon Usage (RSCU) values between essential and non-essential genes in Escherichia coli and thirty-four other bacterial genomes whose gene essentiality features were available in public databases. We observed significant differences in codon usage patterns between essential and non-essential genes for majority of the bacterial genomes and accordingly, ML based classifiers achieved high area under curve (AUC) scores, with a minimum score of 70.0 across twenty-eight organisms. Further, importance of the codons towards classifying genes found to differ among the codons in each genome. Arg codon CGT and Gly codon GGT were observed to be the most preferred codons among essential genes in Escherichia coli. Interestingly, some of the codons like CGT, ATA, GGT and GGG observed to be contributing consistently towards classifying essential genes across thirty-five bacteria genomes studied. In other hand, codons TGY and CAY encoding amino acids Cys and His respectively were among the least contributing codons towards classification among all these bacteria. This study demonstrates the gene essentiality based differences in synonymous codon usage in bacteria genomes and presents a common codon usage pattern across bacteria.

The rate of adaptive molecular evolution in wild and domesticated Saccharomyces cerevisiae populations

Through its fermentative capacities, Saccharomyces cerevisiae was central in the development of civilisation during the Neolithic period, and the yeast remains of importance in industry and biotechnology, giving rise to bona fide domesticated populations. Here, we conduct a population genomic study of domesticated and wild populations of S. cerevisiae. Using coalescent analyses, we report that the effective population size of yeast populations decreased since the divergence with S. paradoxus. We fitted models of distributions of fitness effects to infer the rate of adaptive (ω a ) and non-adaptive (ω na ) non-synonymous substitutions in protein-coding genes. We report an overall limited contribution of positive selection to S. cerevisiae protein evolution, albeit with higher rates of adaptive evolution in wild compared to domesticated populations. Our analyses revealed the signature of background selection and possibly Hill-Robertson interference, as recombination was found to be negatively correlated withω na and positively correlated withω a . However, the effect of recombination onω a was found to be labile, as it is only apparent after removing the impact of codon usage bias on the synonymous site frequency spectrum and disappears if we control for the correlation withω na , suggesting that it could be an artefact of the decreasing population size. Furthermore, the rate of adaptive non-synonymous substitutions is significantly correlated with the residue solvent exposure, a relation that cannot be explained by the population's demography. Together, our results provide a detailed characterisation of adaptive mutations in protein-coding genes across S. cerevisiae populations.

Fitness benefits of a synonymous substitution in an ancient EF-Tu gene depend on the genetic background

Synonymous mutations are changes to DNA sequence, which occur within translated genes but which do not affect the protein sequence. Although often referred to as silent mutations, evidence suggests that synonymous mutations can affect gene expression, mRNA stability, and even translation efficiency. A collection of both experimental and bioinformatic data has shown that synonymous mutations can impact cell phenotype, yet less is known about the molecular mechanisms and potential of beneficial or adaptive effects of such changes within evolved populations. Here, we report a beneficial synonymous mutation acquired via experimental evolution in an essential gene variant encoding the translation elongation factor protein EF-Tu. We demonstrate that this particular synonymous mutation increases EF-Tu mRNA and protein levels as well as global polysome abundance on RNA transcripts. Although presence of the synonymous mutation is clearly causative of such changes, we also demonstrate that fitness benefits are highly contingent on other potentiating mutations present within the genetic background in which the mutation arose. Our results underscore the importance of beneficial synonymous mutations, especially those that affect levels of proteins that are key for cellular processes.IMPORTANCEThis study explores the degree to which synonymous mutations in essential genes can influence adaptation in bacteria. An experimental system whereby an Escherichia coli strain harboring an engineered translation protein elongation factor-Tu (EF-Tu) was subjected to laboratory evolution. We find that a synonymous mutation acquired on the gene encoding for EF-Tu is conditionally beneficial for bacterial fitness. Our findings provide insight into the importance of the genetic background when a synonymous substitution is favored by natural selection and how such changes have the potential to impact evolution when critical cellular processes are involved.

Ribosome inactivation regulates translation elongation in neurons

Cellular plasticity is crucial for adapting to ever-changing stimuli. As a result, cells consistently reshape their translatome, and, consequently, their proteome. The control of translational activity has been thoroughly examined at the stage of translation initiation. However, the regulation of ribosome speed in cells is widely unknown. In this study, we utilized a timed ribosome runoff approach, along with proteomics and transmission electron microscopy, to investigate global translation kinetics in cells. We found that ribosome speeds vary among various cell types, such as astrocytes, induced pluripotent human stem cells, human neural stem cells, and human and rat neurons. Of all cell types studied, mature cortical neurons exhibit the highest rate of translation. This finding is particularly remarkable because mature cortical neurons express the eukaryotic elongation factor 2 (eEF2) at lower levels than other cell types. Neurons solve this conundrum by inactivating a fraction of their ribosomes. As a result, the increase in eEF2 levels leads to a reduction of inactive ribosomes and an enhancement of active ones. Processes that alter the demand for active ribosomes, like neuronal excitation, cause increased inactivation of redundant ribosomes in an eEF2-dependent manner. Our data suggest a novel regulatory mechanism in which neurons dynamically inactivate ribosomes to facilitate translational remodeling. These findings have important implications for developmental brain disorders characterized by, among other things, aberrant translation.

Synonymous codon usage regulates translation initiation

Nonoptimal synonymous codons repress gene expression, but the underlying mechanisms are poorly understood. We and others have previously shown that nonoptimal codons slow translation elongation speeds and thereby trigger messenger RNA (mRNA) degradation. Nevertheless, transcript levels are often insufficient to explain protein levels, suggesting additional mechanisms by which codon usage regulates gene expression. Using reporters in human and Drosophila cells, we find that transcript levels account for less than half of the variation in protein abundance due to codon usage. This discrepancy is explained by translational differences whereby nonoptimal codons repress translation initiation. Nonoptimal transcripts are also less bound by the translation initiation factors eIF4E and eIF4G1, providing a mechanistic explanation for their reduced initiation rates. Importantly, translational repression can occur without mRNA decay and deadenylation, and it does not depend on the known nonoptimality sensor, CNOT3. Our results reveal a potent mechanism of regulation by codon usage where nonoptimal codons repress further rounds of translation.

Mechanisms and pathology of protein misfolding and aggregation

Despite advances in machine learning-based protein structure prediction, we are still far from fully understanding how proteins fold into their native conformation. The conventional notion that polypeptides fold spontaneously to their biologically active states has gradually been replaced by our understanding that cellular protein folding often requires context-dependent guidance from molecular chaperones in order to avoid misfolding. Misfolded proteins can aggregate into larger structures, such as amyloid fibrils, which perpetuate the misfolding process, creating a self-reinforcing cascade. A surge in amyloid fibril structures has deepened our comprehension of how a single polypeptide sequence can exhibit multiple amyloid conformations, known as polymorphism. The assembly of these polymorphs is not a random process but is influenced by the specific conditions and tissues in which they originate. This observation suggests that, similar to the folding of native proteins, the kinetics of pathological amyloid assembly are modulated by interactions specific to cells and tissues. Here, we review the current understanding of how intrinsic protein conformational propensities are modulated by physiological and pathological interactions in the cell to shape protein misfolding and aggregation pathology.

A beneficial synonymous substitution in EF-Tu is contingent on genetic background

Synonymous mutations are changes to DNA sequence that occur within translated genes but which do not affect the protein sequence. Although often referred to as silent mutations, evidence suggests that synonymous mutations can affect gene expression, mRNA stability, and even translation efficiency. A collection of both experimental and bioinformatic data has shown that synonymous mutations can impact cell phenotype, yet less is known about the molecular mechanisms and potential of beneficial or adaptive effects of such changes within evolved populations. Here, we report a beneficial synonymous mutation acquired via experimental evolution in an essential gene variant encoding the translation Elongation Factor protein EF-Tu. We demonstrate that this particular synonymous mutation increases EF-Tu mRNA and protein levels, as well as the polysome abundance on global transcripts. Although presence of the synonymous mutation is clearly causative of such changes, we also demonstrate that fitness benefits are highly contingent on other potentiating mutations present within the genetic background in which the mutation arose. Our results underscore the importance of beneficial synonymous mutations, especially those that affect levels of proteins that are key for cellular processes.

Natural variation in codon bias and mRNA folding strength interact synergistically to modify protein expression in Saccharomyces cerevisiae

Codon bias and mRNA folding strength (mF) are hypothesized molecular mechanisms by which polymorphisms in genes modify protein expression. Natural patterns of codon bias and mF across genes as well as effects of altering codon bias and mF suggest that the influence of these 2 mechanisms may vary depending on the specific location of polymorphisms within a transcript. Despite the central role codon bias and mF may play in natural trait variation within populations, systematic studies of how polymorphic codon bias and mF relate to protein expression variation are lacking. To address this need, we analyzed genomic, transcriptomic, and proteomic data for 22 Saccharomyces cerevisiae isolates, estimated protein accumulation for each allele of 1,620 genes as the log of protein molecules per RNA molecule (logPPR), and built linear mixed-effects models associating allelic variation in codon bias and mF with allelic variation in logPPR. We found that codon bias and mF interact synergistically in a positive association with logPPR, and this interaction explains almost all the effects of codon bias and mF. We examined how the locations of polymorphisms within transcripts influence their effects and found that codon bias primarily acts through polymorphisms in domain-encoding and 3' coding sequences, while mF acts most significantly through coding sequences with weaker effects from untranslated regions. Our results present the most comprehensive characterization to date of how polymorphisms in transcripts influence protein expression.

satmut_utils: a simulation and variant calling package for multiplexed assays of variant effect

The impact of millions of individual genetic variants on molecular phenotypes in coding sequences remains unknown. Multiplexed assays of variant effect (MAVEs) are scalable methods to annotate relevant variants, but existing software lacks standardization, requires cumbersome configuration, and does not scale to large targets. We present satmut_utils as a flexible solution for simulation and variant quantification. We then benchmark MAVE software using simulated and real MAVE data. We finally determine mRNA abundance for thousands of cystathionine beta-synthase variants using two experimental methods. The satmut_utils package enables high-performance analysis of MAVEs and reveals the capability of variants to alter mRNA abundance.

Synonymous Codon Variant Analysis for Autophagic Genes Dysregulated in Neurodegeneration

Neurodegenerative disorders are often a culmination of the accumulation of abnormally folded proteins and defective organelles. Autophagy is a process of removing these defective proteins, organelles, and harmful substances from the body, and it works to maintain homeostasis. If autophagic removal of defective proteins has interfered, it affects neuronal health. Some of the autophagic genes are specifically found to be associated with neurodegenerative phenotypes. Non-functional, mutated, or gene copies having silent mutations, often termed synonymous variants, might explain this. However, these synonymous variant which codes for exactly similar proteins have different translation rates, stability, and gene expression profiling. Hence, it would be interesting to study the pattern of synonymous variant usage. In the study, synonymous variant usage in various transcripts of autophagic genes ATG5, ATG7, ATG8A, ATG16, and ATG17/FIP200 reported to cause neurodegeneration (if dysregulated) is studied. These genes were analyzed for their synonymous variant usage; nucleotide composition; any possible nucleotide skew in a gene; physical properties of autophagic protein including GRAVY and AROMA; hydropathicity; instability index; and frequency of acidic, basic, neutral amino acids; and gene expression level. The study will help understand various evolutionary forces acting on these genes and the possible augmentation of a gene if showing unusual behavior.

Correlation between protein abundance and sequence conservation: what do recent experiments say?

Cells evolve in a space of parameter values set by physical and chemical forces. These constraints create associations among cellular properties. A particularly strong association is the negative correlation between the rate of evolution of proteins and their abundance in the cell. Highly expressed proteins evolve slower than lowly expressed ones. Multiple hypotheses have been put forward to explain this relationship, including, for instance, the requirement for higher mRNA stability, misfolding avoidance, and misinteraction avoidance for highly expressed proteins. Here, we review some of these hypotheses, their predictions, and how they are supported to finally discuss recent experiments that have been performed to test these predictions.

Evolution and codon usage bias of mitochondrial and nuclear genomes in Aspergillus section Flavi

The fungal genus Aspergillus contains a diversity of species divided into taxonomic sections of closely related species. Section Flavi contains 33 species, many of industrial, agricultural, or medical relevance. Here, we analyze the mitochondrial genomes (mitogenomes) of 20 Flavi species-including 18 newly assembled mitogenomes-and compare their evolutionary history and codon usage bias patterns to their nuclear counterparts. Codon usage bias refers to variable frequencies of synonymous codons in coding DNA and is shaped by a balance of neutral processes and natural selection. All mitogenomes were circular DNA molecules with highly conserved gene content and order. As expected, genomic content, including GC content, and genome size differed greatly between mitochondrial and nuclear genomes. Phylogenetic analysis based on 14 concatenated mitochondrial genes predicted evolutionary relationships largely consistent with those predicted by a phylogeny constructed from 2,422 nuclear genes. Comparing similarities in interspecies patterns of codon usage bias between mitochondrial and nuclear genomes showed that species grouped differently by patterns of codon usage bias depending on whether analyses were performed using mitochondrial or nuclear relative synonymous usage values. We found that patterns of codon usage bias at gene level are more similar between mitogenomes of different species than the mitogenome and nuclear genome of the same species. Finally, we inferred that, although most genes-both nuclear and mitochondrial-deviated from the neutral expectation for codon usage, mitogenomes were not under translational selection while nuclear genomes were under moderate translational selection. These results contribute to the study of mitochondrial genome evolution in filamentous fungi.

Pan-cancer analyses of synonymous mutations based on tissue-specific codon optimality

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Developed tissue-specific codon optimality in 29 human tissues.

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Applied these to analyze synonymous mutations in ∼10,000 tumor and normal samples.

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Synonymous mutations frequently increase optimal codons in most cancer types.

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Synonymous mutations frequently increase optimal codons cell cycle-related genes.

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Frequency of optimal codon gain relates to proliferation, DDR deficiency, and survival.

Codon optimality has been demonstrated to be an important determinant of mRNA stability and expression levels in multiple model organisms and human cell lines. However, tissue-specific codon optimality has not been developed to investigate how codon optimality is usually perturbed by somatic synonymous mutations in human cancers. Here, we determined tissue-specific codon optimality in 29 human tissues based on mRNA expression data from the Genotype-Tissue Expression project. We found that optimal codons were associated with differentiation, whereas non-optimal codons were correlated with proliferation. Furthermore, codons biased toward differentiation displayed greater tissue specificity in codon optimality, and the tissue specificity of codon optimality was primarily present in amino acids with high degeneracy of the genetic code. By applying tissue-specific codon optimality to somatic synonymous mutations in 8532 tumor samples across 24 cancer types and to those in 416 normal cells across six human tissues, we found that synonymous mutations frequently increased optimal codons in tumor cells and cancer-related genes (e.g., genes involved in cell cycle). Furthermore, an elevated frequency of optimal codon gain was found to promote tumor cell proliferation in three cancer types characterized by DNA damage repair deficiency and could act as a prognostic biomarker for patients with triple-negative breast cancer. In summary, this study profiled tissue-specific codon optimality in human tissues, revealed alterations in codon optimality caused by synonymous mutations in human cancers, and highlighted the non-negligible role of optimal codon gain in tumorigenesis and therapeutics.

Preferred synonymous codons are translated more accurately: Proteomic evidence, among-species variation, and mechanistic basis

A commonly stated cause of unequal uses of synonymous codons is their differential translational accuracies. A key prediction of this long-standing translational accuracy hypothesis (TAH) of codon usage bias is higher translational accuracies of more frequently used synonymous codons, which, however, has had no direct evidence beyond case studies. Analyzing proteomic data from Escherichia coli, we present direct, global evidence for this prediction. The experimentally measured codon-specific translational accuracies validate a sequence-based proxy; this proxy provides support for the TAH from the vast majority of over 1000 taxa surveyed in all domains of life. We find that the relative translational accuracies of synonymous codons vary substantially among taxa and are strongly correlated with the amounts of cognate transfer RNAs (tRNAs) relative to those of near-cognate tRNAs. These and other observations suggest a model in which selections for translational efficiency and accuracy drive codon usage bias and its coevolution with the tRNA pool.

Quantifying shifts in natural selection on codon usage between protein regions: a population genetics approach

Background

Codon usage bias (CUB), the non-uniform usage of synonymous codons, occurs across all domains of life. Adaptive CUB is hypothesized to result from various selective pressures, including selection for efficient ribosome elongation, accurate translation, mRNA secondary structure, and/or protein folding. Given the critical link between protein folding and protein function, numerous studies have analyzed the relationship between codon usage and protein structure. The results from these studies have often been contradictory, likely reflecting the differing methods used for measuring codon usage and the failure to appropriately control for confounding factors, such as differences in amino acid usage between protein structures and changes in the frequency of different structures with gene expression.

Results

Here we take an explicit population genetics approach to quantify codon-specific shifts in natural selection related to protein structure in S. cerevisiae and E. coli. Unlike other metrics of codon usage, our approach explicitly separates the effects of natural selection, scaled by gene expression, and mutation bias while naturally accounting for a region’s amino acid usage. Bayesian model comparisons suggest selection on codon usage varies only slightly between helix, sheet, and coil secondary structures and, similarly, between structured and intrinsically-disordered regions. Similarly, in contrast to prevous findings, we find selection on codon usage only varies slightly at the termini of helices in E. coli. Using simulated data, we show this previous work indicating “non-optimal” codons are enriched at the beginning of helices in S. cerevisiae was due to failure to control for various confounding factors (e.g. amino acid biases, gene expression, etc.), and rather than selection to modulate cotranslational folding.

Conclusions

Our results reveal a weak relationship between codon usage and protein structure, indicating that differences in selection on codon usage between structures are slight. In addition to the magnitude of differences in selection between protein structures being slight, the observed shifts appear to be idiosyncratic and largely codon-specific rather than systematic reversals in the nature of selection. Overall, our work demonstrates the statistical power and benefits of studying selective shifts on codon usage or other genomic features from an explicitly evolutionary approach. Limitations of this approach and future potential research avenues are discussed.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12864-022-08635-0).

Kingdom-Wide Analysis of Fungal Transcriptomes and tRNAs Reveals Conserved Patterns of Adaptive Evolution

Protein-coding genes evolved codon usage bias due to the combined but uneven effects of adaptive and nonadaptive influences. Studies in model fungi agree on codon usage bias as an adaptation for fine-tuning gene expression levels; however, such knowledge is lacking for most other fungi. Our comparative genomics analysis of over 450 species supports codon usage and transfer RNAs (tRNAs) as coadapted for translation speed and this is most likely a realization of convergent evolution. Rather than drift, phylogenetic reconstruction inferred adaptive radiation as the best explanation for the variation of interspecific codon usage bias. Although the phylogenetic signals for individual codon and tRNAs frequencies are lower than expected by genetic drift, we found remarkable conservation of highly expressed genes being codon optimized for translation by the most abundant tRNAs, especially by inosine-modified tRNAs. As an application, we present a sequence-to-expression neural network that uses codons to reliably predict highly expressed transcripts. The kingdom Fungi, with over a million species, includes many key players in various ecosystems and good targets for biotechnology. Collectively, our results have implications for better understanding the evolutionary success of fungi, as well as informing the biosynthetic manipulation of fungal genes.

Molecular determination of progesterone receptor's PROGINS allele (Alu insertion) and its association with the predisposition and susceptibility to polycystic ovary syndrome (PCOS)

Polycystic ovary syndrome, previously known as Stein-Leventhal syndrome, is associated with altered reproductive endocrinology, predisposing a young woman towards the risk of PCOS. It has a prevalence of 6-20% among the reproductive-age women. Progesterone is a key hormone in the pathophysiology of PCOS and patients show diminished response (progesterone resistance), implicating the role of progesterone receptor (PR) as a factor in the disease etiology and prognosis. In this case-control study, we have used mutation-specific PCR (confirmed by Sanger sequencing) to detect the presence of a pathologically significant PR polymorphic variant called as PROGINS. The variant has an Alu insertion in intron G and has two SNPs in exon 4 and exon 5, with all the three aberrations in complete disequilibrium. Our results demonstrated a statistically significant difference in the frequencies of PROGINS between the PCOS patients and healthy controls (p = 0.047). The frequencies of the genotypes CC (A1/A1), CT (A1/A2), and TT (A2/A2) in patients were 74.50%, 20.58%, and 4.90%, and in healthy controls they were 87.28%, 11%, and 1.69%, respectively. Our results put forward two determining factors with regard to PCOS: (i) the frequency of PROGINS allele was significantly higher among PCOS patients compared to the healthy matched controls (0.15 vs 0.07) in the studied population, (ii) the PROGIN allele was significantly associated with the lower levels of serum progesterone in PCOS patients (p < 0.003). The findings are conspicuous as these relate the PROGINS variant to the increased susceptibility of PCOS and might explain the progesterone resistance in patients.

Glue Genes Are Subjected to Diverse Selective Forces during Drosophila Development

Molecular evolutionary studies usually focus on genes with clear roles in adult fitness or on developmental genes expressed at multiple time points during the life of the organism. Here, we examine the evolutionary dynamics of Drosophila glue genes, a set of eight genes tasked with a singular primary function during a specific developmental stage: the production of glue that allows animal pupa to attach to a substrate for several days during metamorphosis. Using phenotypic assays and available data from transcriptomics, PacBio genomes, and sequence variation from global populations, we explore the selective forces acting on glue genes within the cosmopolitan Drosophila melanogaster species and its five closely related species, D. simulans, D. sechellia, D. mauritiana, D. yakuba, and D. teissieri. We observe a three-fold difference in glue adhesion between the least and the most adhesive D. melanogaster strain, indicating a strong genetic component to phenotypic variation. These eight glue genes are among the most highly expressed genes in salivary glands yet they display no notable codon bias. New copies of Sgs3 and Sgs7 are found in D. yakuba and D. teissieri with the Sgs3 coding sequence evolving rapidly after duplication in the D. yakuba branch. Multiple sites along the various glue genes appear to be constrained. Our population genetics analysis in D. melanogaster suggests signals of local adaptive evolution for Sgs3, Sgs5, and Sgs5bis and traces of selective sweeps for Sgs1, Sgs3, Sgs7, and Sgs8. Our work shows that stage-specific genes can be subjected to various dynamic evolutionary forces.

Codon Usage Bias in Autophagy-Related Gene 13 in Eukaryotes: Uncovering the Genetic Divergence by the Interplay Between Nucleotides and Codon Usages

Synonymous codon usage bias is a universal characteristic of genomes across various organisms. Autophagy-related gene 13 (atg13) is one essential gene for autophagy initiation, yet the evolutionary trends of the atg13 gene at the usages of nucleotide and synonymous codon remains unexplored. According to phylogenetic analyses for the atg13 gene of 226 eukaryotic organisms at the nucleotide and amino acid levels, it is clear that their nucleotide usages exhibit more genetic information than their amino acid usages. Specifically, the overall nucleotide usage bias quantified by information entropy reflected that the usage biases at the first and second codon positions were stronger than those at the third position of the atg13 genes. Furthermore, the bias level of nucleotide ‘G’ usage is highest, while that of nucleotide ‘C’ usage is lowest in the atg13 genes. On top of that, genetic features represented by synonymous codon usage exhibits a species-specific pattern on the evolution of the atg13 genes to some extent. Interestingly, the codon usages of atg13 genes in the ancestor animals (Latimeria chalumnae, Petromyzon marinus, and Rhinatrema bivittatum) are strongly influenced by mutation pressure from nucleotide composition constraint. However, the distributions of nucleotide composition at different codon positions in the atg13 gene display that natural selection still dominates atg13 codon usages during organisms’ evolution.

Ribosome dynamics and mRNA turnover, a complex relationship under constant cellular scrutiny

Eukaryotic gene expression is closely regulated by translation and turnover of mRNAs. Recent advances highlight the importance of translation in the control of mRNA degradation, both for aberrant and apparently normal mRNAs. During translation, the information contained in mRNAs is decoded by ribosomes, one codon at a time, and tRNAs, by specifically recognizing codons, translate the nucleotide code into amino acids. Such a decoding step does not process regularly, with various obstacles that can hinder ribosome progression, then leading to ribosome stalling or collisions. The progression of ribosomes is constantly monitored by the cell which has evolved several translation-dependent mRNA surveillance pathways, including nonsense-mediated decay (NMD), no-go decay (NGD), and non-stop decay (NSD), to degrade certain problematic mRNAs and the incomplete protein products. Recent progress in sequencing and ribosome profiling has made it possible to discover new mechanisms controlling ribosome dynamics, with numerous crosstalks between translation and mRNA decay. We discuss here various translation features critical for mRNA decay, with particular focus on current insights from the complexity of the genetic code and also the emerging role for the ribosome as a regulatory hub orchestrating mRNA decay, quality control, and stress signaling. Even if the interplay between mRNA translation and degradation is no longer to be demonstrated, a better understanding of their precise coordination is worthy of further investigation. This article is categorized under: RNA Turnover and Surveillance > Regulation of RNA Stability Translation > Translation Regulation RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes.

Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns

Despite the wide perception that microbes have rapid growth rates, many environments like seawater and soil are often dominated by microorganisms that can only grow very slowly. Our knowledge about growth is necessarily biased toward easily culturable organisms, which tend to be those that grow fast, because microbial growth rates have traditionally been measured using laboratory growth experiments. However, how are potential growth rates distributed in nature? Using genomic data, we predicted the growth rates of over 200,000 organisms, including many as yet uncultivated species. These data reveal how current culture collections are strongly biased toward fast-growing organisms. Finally, we noticed a bimodal distribution of maximal growth rates, suggesting a natural division of microbial growth strategies into two classes.

Maximal growth rate is a basic parameter of microbial lifestyle that varies over several orders of magnitude, with doubling times ranging from a matter of minutes to multiple days. Growth rates are typically measured using laboratory culture experiments. Yet, we lack sufficient understanding of the physiology of most microbes to design appropriate culture conditions for them, severely limiting our ability to assess the global diversity of microbial growth rates. Genomic estimators of maximal growth rate provide a practical solution to survey the distribution of microbial growth potential, regardless of cultivation status. We developed an improved maximal growth rate estimator and predicted maximal growth rates from over 200,000 genomes, metagenome-assembled genomes, and single-cell amplified genomes to survey growth potential across the range of prokaryotic diversity; extensions allow estimates from 16S rRNA sequences alone as well as weighted community estimates from metagenomes. We compared the growth rates of cultivated and uncultivated organisms to illustrate how culture collections are strongly biased toward organisms capable of rapid growth. Finally, we found that organisms naturally group into two growth classes and observed a bias in growth predictions for extremely slow-growing organisms. These observations ultimately led us to suggest evolutionary definitions of oligotrophy and copiotrophy based on the selective regime an organism occupies. We found that these growth classes are associated with distinct selective regimes and genomic functional potentials.

Some theoretical aspects of reprogramming the standard genetic code

Reprogramming of the standard genetic code to include non-canonical amino acids (ncAAs) opens new prospects for medicine, industry, and biotechnology. There are several methods of code engineering, which allow us for storing new genetic information in DNA sequences and producing proteins with new properties. Here, we provided a theoretical background for the optimal genetic code expansion, which may find application in the experimental design of the genetic code. We assumed that the expanded genetic code includes both canonical and non-canonical information stored in 64 classical codons. What is more, the new coding system is robust to point mutations and minimizes the possibility of reversion from the new to old information. In order to find such codes, we applied graph theory to analyze the properties of optimal codon sets. We presented the formal procedure in finding the optimal codes with various number of vacant codons that could be assigned to new amino acids. Finally, we discussed the optimal number of the newly incorporated ncAAs and also the optimal size of codon groups that can be assigned to ncAAs.

Whole-Genome Transformation Promotes tRNA Anticodon Suppressor Mutations under Stress

tRNAs are encoded by a large gene family, usually with several isogenic tRNAs interacting with the same codon. Mutations in the anticodon region of other tRNAs can overcome specific tRNA deficiencies. Phylogenetic analysis suggests that such mutations have occurred in evolution, but the driving force is unclear. We show that in yeast suppressor mutations in other tRNAs are able to overcome deficiency of the essential TRT2-encoded tRNAThrCGU at high temperature (40°C). Surprisingly, these tRNA suppressor mutations were obtained after whole-genome transformation with DNA from thermotolerant Kluyveromyces marxianus or Ogataea polymorpha strains but from which the mutations did apparently not originate. We suggest that transient presence of donor DNA in the host facilitates proliferation at high temperature and thus increases the chances for occurrence of spontaneous mutations suppressing defective growth at high temperature. Whole-genome sequence analysis of three transformants revealed only four to five nonsynonymous mutations of which one causing TRT2 anticodon stem stabilization and two anticodon mutations in non-threonyl-tRNAs, tRNALysCUU and tRNAeMetCAU, were causative. Both anticodon mutations suppressed lethality of TRT2 deletion and apparently caused the respective tRNAs to become novel substrates for threonyl-tRNA synthetase. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) data could not detect any significant mistranslation, and reverse transcription-quantitative PCR results contradicted induction of the unfolded protein response. We suggest that stress conditions have been a driving force in evolution for the selection of anticodon-switching mutations in tRNAs as revealed by phylogenetic analysis.IMPORTANCE In this work, we have identified for the first time the causative elements in a eukaryotic organism introduced by applying whole-genome transformation and responsible for the selectable trait of interest, i.e., high temperature tolerance. Surprisingly, the whole-genome transformants contained just a few single nucleotide polymorphisms (SNPs), which were unrelated to the sequence of the donor DNA. In each of three independent transformants, we have identified a SNP in a tRNA, either stabilizing the essential tRNAThrCGU at high temperature or switching the anticodon of tRNALysCUU or tRNAeMetCAU into CGU, which is apparently enough for in vivo recognition by threonyl-tRNA synthetase. LC-MS/MS analysis indeed indicated absence of significant mistranslation. Phylogenetic analysis showed that similar mutations have occurred throughout evolution and we suggest that stress conditions may have been a driving force for their selection. The low number of SNPs introduced by whole-genome transformation may favor its application for improvement of industrial yeast strains.

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

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

Synonymous but Not Silent: The Codon Usage Code for Gene Expression and Protein Folding

Codon usage bias, the preference for certain synonymous codons, is found in all genomes. Although synonymous mutations were previously thought to be silent, a large body of evidence has demonstrated that codon usage can play major roles in determining gene expression levels and protein structures. Codon usage influences translation elongation speed and regulates translation efficiency and accuracy. Adaptation of codon usage to tRNA expression determines the proteome landscape. In addition, codon usage biases result in nonuniform ribosome decoding rates on mRNAs, which in turn influence the cotranslational protein folding process that is critical for protein function in diverse biological processes. Conserved genome-wide correlations have also been found between codon usage and protein structures. Furthermore, codon usage is a major determinant of mRNA levels through translation-dependent effects on mRNA decay and translation-independent effects on transcriptional and posttranscriptional processes. Here, we discuss the multifaceted roles and mechanisms of codon usage in different gene regulatory processes.

Translational Control by Ribosome Pausing in Bacteria: How a Non-uniform Pace of Translation Affects Protein Production and Folding

Protein homeostasis of bacterial cells is maintained by coordinated processes of protein production, folding, and degradation. Translational efficiency of a given mRNA depends on how often the ribosomes initiate synthesis of a new polypeptide and how quickly they read the coding sequence to produce a full-length protein. The pace of ribosomes along the mRNA is not uniform: periods of rapid synthesis are separated by pauses. Here, we summarize recent evidence on how ribosome pausing affects translational efficiency and protein folding. We discuss the factors that slow down translation elongation and affect the quality of the newly synthesized protein. Ribosome pausing emerges as important factor contributing to the regulatory programs that ensure the quality of the proteome and integrate the cellular and environmental cues into regulatory circuits of the cell.

Network analysis of synonymous codon usage

MOTIVATION

Most amino acids are encoded by multiple synonymous codons, some of which are used more rarely than others. Analyses of positions of such rare codons in protein sequences revealed that rare codons can impact co-translational protein folding and that positions of some rare codons are evolutionarily conserved. Analyses of their positions in protein 3-dimensional structures, which are richer in biochemical information than sequences alone, might further explain the role of rare codons in protein folding.

RESULTS

We model protein structures as networks and use network centrality to measure the structural position of an amino acid. We first validate that amino acids buried within the structural core are network-central, and those on the surface are not. Then, we study potential differences between network centralities and thus structural positions of amino acids encoded by conserved rare, non-conserved rare and commonly used codons. We find that in 84% of proteins, the three codon categories occupy significantly different structural positions. We examine protein groups showing different codon centrality trends, i.e. different relationships between structural positions of the three codon categories. We see several cases of all proteins from our data with some structural or functional property being in the same group. Also, we see a case of all proteins in some group having the same property. Our work shows that codon usage is linked to the final protein structure and thus possibly to co-translational protein folding.

AVAILABILITY AND IMPLEMENTATION

https://nd.edu/∼cone/CodonUsage/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Large-scale in silico mutagenesis experiments reveal optimization of genetic code and codon usage for protein mutational robustness

BACKGROUND

How, and the extent to which, evolution acts on DNA and protein sequences to ensure mutational robustness and evolvability is a long-standing open question in the field of molecular evolution. We addressed this issue through the first structurome-scale computational investigation, in which we estimated the change in folding free energy upon all possible single-site mutations introduced in more than 20,000 protein structures, as well as through available experimental stability and fitness data.

RESULTS

At the amino acid level, we found the protein surface to be more robust against random mutations than the core, this difference being stronger for small proteins. The destabilizing and neutral mutations are more numerous in the core and on the surface, respectively, whereas the stabilizing mutations are about 4% in both regions. At the genetic code level, we observed smallest destabilization for mutations that are due to substitutions of base III in the codon, followed by base I, bases I+III, base II, and other multiple base substitutions. This ranking highly anticorrelates with the codon-anticodon mispairing frequency in the translation process. This suggests that the standard genetic code is optimized to limit the impact of random mutations, but even more so to limit translation errors. At the codon level, both the codon usage and the usage bias appear to optimize mutational robustness and translation accuracy, especially for surface residues.

CONCLUSION

Our results highlight the non-universality of mutational robustness and its multiscale dependence on protein features, the structure of the genetic code, and the codon usage. Our analyses and approach are strongly supported by available experimental mutagenesis data.

The relationship between protein domains and homopeptides in the Plasmodium falciparum proteome

The proteome of the malaria parasite Plasmodium falciparum is notable for the pervasive occurrence of homopeptides or low-complexity regions (i.e., regions that are made from a small subset of amino-acid residue types). The most prevalent of these are made from residues encoded by adenine/thymidine (AT)-rich codons, in particular asparagine. We examined homopeptide occurrences within protein domains in P. falciparum. Homopeptide enrichments occur for hydrophobic (e.g., valine), or small residues (alanine or glycine) in short spans (<5 residues), but these enrichments disappear for longer lengths. We observe that short asparagine homopeptides (<10 residues long) have a dramatic relative depletion inside protein domains, indicating some selective constraint to keep them from forming. We surmise that this is possibly linked to co-translational protein folding, although there are specific protein domains that are enriched in longer asparagine homopeptides (≥10 residues) indicating a functional linkage for specific poly-asparagine tracts. Top gene ontology functional category enrichments for homopeptides associated with diverse protein domains include “vesicle-mediated transport”, and “DNA-directed 5′-3′ RNA polymerase activity”, with various categories linked to “binding” evidencing significant homopeptide depletions. Also, in general homopeptides are substantially enriched in the parts of protein domains that are near/in IDRs. The implications of these findings are discussed.

A code within the genetic code: codon usage regulates co-translational protein folding

The genetic code is degenerate, and most amino acids are encoded by two to six synonymous codons. Codon usage bias, the preference for certain synonymous codons, is a universal feature of all genomes examined. Synonymous codon mutations were previously thought to be silent; however, a growing body evidence now shows that codon usage regulates protein structure and gene expression through effects on co-translational protein folding, translation efficiency and accuracy, mRNA stability, and transcription. Codon usage regulates the speed of translation elongation, resulting in non-uniform ribosome decoding rates on mRNAs during translation that is adapted to co-translational protein folding process. Biochemical and genetic evidence demonstrate that codon usage plays an important role in regulating protein folding and function in both prokaryotic and eukaryotic organisms. Certain protein structural types are more sensitive than others to the effects of codon usage on protein folding, and predicted intrinsically disordered domains are more prone to misfolding caused by codon usage changes than other domain types. Bioinformatic analyses revealed that gene codon usage correlates with different protein structures in diverse organisms, indicating the existence of a codon usage code for co-translational protein folding. This review focuses on recent literature on the role and mechanism of codon usage in regulating translation kinetics and co-translational protein folding.

SARS-CoV-2 Codon Usage Bias Downregulates Host Expressed Genes With Similar Codon Usage

Severe acute respiratory syndrome has spread quickly throughout the world and was declared a pandemic by the World Health Organization (WHO). The pathogenic agent is a new coronavirus (SARS-CoV-2) that infects pulmonary cells with great effectiveness. In this study we focus on the codon composition for the viral protein synthesis and its relationship with the protein synthesis of the host. Our analysis reveals that SARS-CoV-2 preferred codons have poor representation of G or C nucleotides in the third position, a characteristic which could result in an unbalance in the tRNAs pools of the infected cells with serious implications in host protein synthesis. By integrating this observation with proteomic data from infected cells, we observe a reduced translation rate of host proteins associated with highly expressed genes and that they share the codon usage bias of the virus. The functional analysis of these genes suggests that this mechanism of epistasis can contribute to understanding how this virus evades the immune response and the etiology of some deleterious collateral effect as a result of the viral replication. In this manner, our finding contributes to the understanding of the SARS-CoV-2 pathogeny and could be useful for the design of a vaccine based on the live attenuated strategy.

Hidden patterns of codon usage bias across kingdoms

The genetic code is necessarily degenerate with 64 possible nucleotide triplets being translated into 20 amino acids. Eighteen out of the 20 amino acids are encoded by multiple synonymous codons. While synonymous codons are clearly equivalent in terms of the information they carry, it is now well established that they are used in a biased fashion. There is currently no consensus as to the origin of this bias. Drawing on ideas from stochastic thermodynamics we derive from first principles a mathematical model describing the statistics of codon usage bias. We show that the model accurately describes the distribution of codon usage bias of genomes in the fungal and bacterial kingdoms. Based on it, we derive a new computational measure of codon usage bias-the distance D capturing two aspects of codon usage bias: (i) differences in the genome-wide frequency of codons and (ii) apparent non-random distributions of codons across mRNAs. By means of large scale computational analysis of over 900 species across two kingdoms of life, we demonstrate that our measure provides novel biological insights. Specifically, we show that while codon usage bias is clearly based on heritable traits and closely related species show similar degrees of bias, there is considerable variation in the magnitude of D within taxonomic classes suggesting that the contribution of sequence-level selection to codon bias varies substantially within relatively confined taxonomic groups. Interestingly, commonly used model organisms are near the median for values of D for their taxonomic class, suggesting that they may not be good representative models for species with more extreme D, which comprise organisms of medical and agricultural interest. We also demonstrate that amino acid specific patterns of codon usage are themselves quite variable between branches of the tree of life, and that some of this variability correlates with organismal tRNA content.

Predicting Functional Effects of Synonymous Variants: A Systematic Review and Perspectives

Recent advances in high-throughput experimentation have put the exploration of genome sequences at the forefront of precision medicine. In an effort to interpret the sequencing data, numerous computational methods have been developed for evaluating the effects of genome variants. Interestingly, despite the fact that every person has as many synonymous (sSNV) as non-synonymous single nucleotide variants, our ability to predict their effects is limited. The paucity of experimentally tested sSNV effects appears to be the limiting factor in development of such methods. Here, we summarize the details and evaluate the performance of nine existing computational methods capable of predicting sSNV effects. We used a set of observed and artificially generated variants to approximate large scale performance expectations of these tools. We note that the distribution of these variants across amino acid and codon types suggests purifying evolutionary selection retaining generated variants out of the observed set; i.e., we expect the generated set to be enriched for deleterious variants. Closer inspection of the relationship between the observed variant frequencies and the associated prediction scores identifies predictor-specific scoring thresholds of reliable effect predictions. Notably, across all predictors, the variants scoring above these thresholds were significantly more often generated than observed. which confirms our assumption that the generated set is enriched for deleterious variants. Finally, we find that while the methods differ in their ability to identify severe sSNV effects, no predictor appears capable of definitively recognizing subtle effects of such variants on a large scale.

Codon bias confers stability to human mRNAs

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

Nature and Regulation of Protein Folding on the Ribosome

Co-translational protein folding is an essential process by which cells ensure the safe and efficient production and assembly of new proteins in their functional native states following biosynthesis on the ribosome. In this review, we describe recent progress in probing the changes during protein synthesis of the free energy landscapes that underlie co-translational folding and discuss the critical coupling between these landscapes and the rate of translation that ultimately determines the success or otherwise of the folding process. Recent developments have revealed a variety of mechanisms by which both folding and translation can be modulated or regulated, and we discuss how these effects are utilised by the cell to optimise the outcome of protein biosynthesis.

Variation and selection on codon usage bias across an entire subphylum

Variation in synonymous codon usage is abundant across multiple levels of organization: between codons of an amino acid, between genes in a genome, and between genomes of different species. It is now well understood that variation in synonymous codon usage is influenced by mutational bias coupled with both natural selection for translational efficiency and genetic drift, but how these processes shape patterns of codon usage bias across entire lineages remains unexplored. To address this question, we used a rich genomic data set of 327 species that covers nearly one third of the known biodiversity of the budding yeast subphylum Saccharomycotina. We found that, while genome-wide relative synonymous codon usage (RSCU) for all codons was highly correlated with the GC content of the third codon position (GC3), the usage of codons for the amino acids proline, arginine, and glycine was inconsistent with the neutral expectation where mutational bias coupled with genetic drift drive codon usage. Examination between genes' effective numbers of codons and their GC3 contents in individual genomes revealed that nearly a quarter of genes (381,174/1,683,203; 23%), as well as most genomes (308/327; 94%), significantly deviate from the neutral expectation. Finally, by evaluating the imprint of translational selection on codon usage, measured as the degree to which genes' adaptiveness to the tRNA pool were correlated with selective pressure, we show that translational selection is widespread in budding yeast genomes (264/327; 81%). These results suggest that the contribution of translational selection and drift to patterns of synonymous codon usage across budding yeasts varies across codons, genes, and genomes; whereas drift is the primary driver of global codon usage across the subphylum, the codon bias of large numbers of genes in the majority of genomes is influenced by translational selection.

Codon Usage Bias in Animals: Disentangling the Effects of Natural Selection, Effective Population Size, and GC-Biased Gene Conversion

Selection on codon usage bias is well documented in a number of microorganisms. Whether codon usage is also generally shaped by natural selection in large organisms, despite their relatively small effective population size (Ne), is unclear. In animals, the population genetics of codon usage bias has only been studied in a handful of model organisms so far, and can be affected by confounding, nonadaptive processes such as GC-biased gene conversion and experimental artefacts. Using population transcriptomics data, we analyzed the relationship between codon usage, gene expression, allele frequency distribution, and recombination rate in 30 nonmodel species of animals, each from a different family, covering a wide range of effective population sizes. We disentangled the effects of translational selection and GC-biased gene conversion on codon usage by separately analyzing GC-conservative and GC-changing mutations. We report evidence for effective translational selection on codon usage in large-Ne species of animals, but not in small-Ne ones, in agreement with the nearly neutral theory of molecular evolution. C- and T-ending codons tend to be preferred over synonymous G- and A-ending ones, for reasons that remain to be determined. In contrast, we uncovered a conspicuous effect of GC-biased gene conversion, which is widespread in animals and the main force determining the fate of AT↔GC mutations. Intriguingly, the strength of its effect was uncorrelated with Ne.

The stop-and-go traffic regulating protein biogenesis: How translation kinetics controls proteostasis

Generating a functional proteome requires the ribosome to carefully regulate disparate co-translational processes that determine the fate of nascent polypeptides. With protein synthesis being energetically expensive, the ribosome must balance the costs of efficiently making a protein with those of properly folding it. Emerging as a primary means of regulating this trade-off is the nonuniform rate of translation elongation that defines translation kinetics. The varying speeds with which the ribosome progresses along a transcript have been implicated in several aspects of protein biogenesis, including co-translational protein folding and translational fidelity, as well as gene expression by mediating mRNA decay and protein quality control pathways. The optimal translation kinetics required to efficiently execute these processes can be distinct. Thus, the ribosome is tasked with tightly regulating translation kinetics to balance these processes while maintaining adaptability for changing cellular conditions. In this review, we first discuss the regulatory role of translation elongation in protein biogenesis and what factors influence elongation kinetics. We then describe how changes in translation kinetics signal downstream pathways that dictate the fate of nascent polypeptides. By regulating these pathways, the kinetics of translation elongation has emerged as a critical tool for driving gene expression and maintaining proteostasis through varied mechanisms, including nascent chain folding and binding different ribosome-associated machinery. Indeed, a growing number of examples demonstrate the important role of local changes in elongation kinetics in modulating the pathophysiology of human disease.

Differences in Codon Usage Bias between Photosynthesis-Related Genes and Genetic System-Related Genes of Chloroplast Genomes in Cultivated and Wild Solanum Species

Solanum is one of the largest genera, including two important crops-potato (Solanum tuberosum) and tomato (Solanum lycopersicum). In this study we compared the chloroplast codon usage bias (CUB) among 12 Solanum species, between photosynthesis-related genes (Photo-genes) and genetic system-related genes (Genet-genes), and between cultivated species and wild relatives. The Photo-genes encode proteins for photosystems, the photosynthetic electron transport chain, and RuBisCO, while the Genet-genes encode proteins for ribosomal subunits, RNA polymerases, and maturases. The following findings about the Solanum chloroplast genome CUB were obtained: (1) the nucleotide composition, gene expression, and selective pressure are identified as the main factors affecting chloroplast CUB; (2) all these 12 chloroplast genomes prefer A/U over G/C and pyrimidines over purines at the third-base of codons; (3) Photo-genes have higher codon adaptation indexes than Genet-genes, indicative of a higher gene expression level and a stronger adaptation of Photo-genes; (4) gene function is the primary factor affecting CUB of Photo-genes but not Genet-genes; (5) Photo-genes prefer pyrimidine over purine, whereas Genet-genes favor purine over pyrimidine, at the third position of codons; (6) Photo-genes are mainly affected by the selective pressure, whereas Genet-genes are under the underlying mutational bias; (7) S. tuberosum is more similar with Solanum commersonii than with Solanum bulbocastanum; (8) S. lycopersicum is greatly different from the analyzed seven wild relatives; (9) the CUB in codons for valine, aspartic acid, and threonine are the same between the two crop species, S. tuberosum and S. lycopersicum. These findings suggest that the chloroplast CUB contributed to the differential requirement of gene expression activity and function between Photo-genes and Genet-genes and to the performance of cultivated potato and tomato.

Refining the Ambush Hypothesis: Evidence That GC- and AT-Rich Bacteria Employ Different Frameshift Defence Strategies

Stop codons are frequently selected for beyond their regular termination function for error control. The “ambush hypothesis” proposes out-of-frame stop codons (OSCs) terminating frameshifted translations are selected for. Although early indirect evidence was partially supportive, recent evidence suggests OSC frequencies are not exceptional when considering underlying nucleotide content. However, prior null tests fail to control amino acid/codon usages or possible local mutational biases. We therefore return to the issue using bacterial genomes, considering several tests defining and testing against a null. We employ simulation approaches preserving amino acid order but shuffling synonymous codons or preserving codons while shuffling amino acid order. Additionally, we compare codon usage in amino acid pairs, where one codon can but the next, otherwise identical codon, cannot encode an OSC. OSC frequencies exceed expectations typically in AT-rich genomes, the +1 frame and for TGA/TAA but not TAG. With this complex evidence, simply rejecting or accepting the ambush hypothesis is not warranted. We propose a refined post hoc model, whereby AT-rich genomes have more accidental frameshifts, handled by RF2–RF3 complexes (associated with TGA/TAA) and are mostly +1 (or −2) slips. Supporting this, excesses positively correlate with in silico predicted frameshift probabilities. Thus, we propose a more viable framework, whereby genomes broadly adopt one of the two strategies to combat frameshifts: preventing frameshifting (GC-rich) or permitting frameshifts but minimizing impacts when most are caught early (AT-rich). Our refined framework holds promise yet some features, such as the bias of out-of-frame sense codons, remain unexplained.

The fitness landscape of the codon space across environments

Fitness landscapes map the relationship between genotypes and fitness. However, most fitness landscape studies ignore the genetic architecture imposed by the codon table and thereby neglect the potential role of synonymous mutations. To quantify the fitness effects of synonymous mutations and their potential impact on adaptation on a fitness landscape, we use a new software based on Bayesian Monte Carlo Markov Chain methods and re-estimate selection coefficients of all possible codon mutations across 9 amino acid positions in Saccharomyces cerevisiae Hsp90 across 6 environments. We quantify the distribution of fitness effects of synonymous mutations and show that it is dominated by many mutations of small or no effect and few mutations of larger effect. We then compare the shape of the codon fitness landscape across amino acid positions and environments, and quantify how the consideration of synonymous fitness effects changes the evolutionary dynamics on these fitness landscapes. Together these results highlight a possible role of synonymous mutations in adaptation and indicate the potential mis-inference when they are neglected in fitness landscape studies.

Repertoires of tRNAs: The Couplers of Genomics and Proteomics

The pool of transfer RNA (tRNA) molecules in cells allows the ribosome to decode genetic information. This repertoire of molecular decoders is positioned in the crossroad of the genome, the transcriptome, and the proteome. Omics and systems biology now allow scientists to explore the entire repertoire of tRNAs of many organisms, revealing basic exciting biology. The tRNA gene set of hundreds of species is now characterized, in addition to the tRNA genes of organelles and viruses. Genes encoding tRNAs for certain anticodon types appear in dozens of copies in a genome, while others are universally absent from any genome. Transcriptome measurement of tRNAs is challenging, but in recent years new technologies have allowed researchers to determine the dynamic expression patterns of tRNAs. These advances reveal that availability of ready-to-translate tRNA molecules is highly controlled by several transcriptional and posttranscriptional regulatory processes. This regulation shapes the proteome according to the cellular state. The tRNA pool profoundly impacts many aspects of cellular and organismal life, including protein expression level, translation accuracy, adequacy of folding, and even mRNA stability. As a result, the shape of the tRNA pool affects organismal health and may participate in causing conditions such as cancer and neurological conditions.

Codon usage clusters correlation: towards protein solubility prediction in heterologous expression systems in E. coli

Production of soluble recombinant proteins is crucial to the development of industry and basic research. However, the aggregation due to the incorrect folding of the nascent polypeptides is still a mayor bottleneck. Understanding the factors governing protein solubility is important to grasp the underlying mechanisms and improve the design of recombinant proteins. Here we show a quantitative study of the expression and solubility of a set of proteins from Bizionia argentinensis. Through the analysis of different features known to modulate protein production, we defined two parameters based on the %MinMax algorithm to compare codon usage clusters between the host and the target genes. We demonstrate that the absolute difference between all %MinMax frequencies of the host and the target gene is significantly negatively correlated with protein expression levels. But most importantly, a strong positive correlation between solubility and the degree of conservation of codons usage clusters is observed for two independent datasets. Moreover, we evince that this correlation is higher in codon usage clusters involved in less compact protein secondary structure regions. Our results provide important tools for protein design and support the notion that codon usage may dictate translation rate and modulate co-translational folding.

corseq: fast and efficient identification of favoured codons from next generation sequencing reads

Background

Optimization of transgene expression can be achieved by designing coding sequences with the synonymous codon usage of genes which are highly expressed in the host organism. The identification of the so-called “favoured codons” generally requires the access to either the genome or the coding sequences and the availability of expression data.

Results

Here we describe corseq, a fast and reliable software for detecting the favoured codons directly from RNAseq data without prior knowledge of genomic sequence or gene annotation. The presented tool allows the inference of codons that are preferentially used in highly expressed genes while estimating the transcripts abundance by a new kmer based approach. corseq is implemented in Python and runs under any operating system. The software requires the Biopython 1.65 library (or later versions) and is available under the ‘GNU General Public License version 3’ at the project webpage https://sourceforge.net/projects/corseq/files.

Conclusion

corseq represents a faster and easy-to-use alternative for the detection of favoured codons in non model organisms.

Codon usage of highly expressed genes affects proteome-wide translation efficiency

Although the genetic code is redundant, synonymous codons for the same amino acid are not used with equal frequencies in genomes, a phenomenon termed "codon usage bias." Previous studies have demonstrated that synonymous changes in a coding sequence can exert significant cis effects on the gene's expression level. However, whether the codon composition of a gene can also affect the translation efficiency of other genes has not been thoroughly explored. To study how codon usage bias influences the cellular economy of translation, we massively converted abundant codons to their rare synonymous counterpart in several highly expressed genes in Escherichia coli This perturbation reduces both the cellular fitness and the translation efficiency of genes that have high initiation rates and are naturally enriched with the manipulated codon, in agreement with theoretical predictions. Interestingly, we could alleviate the observed phenotypes by increasing the supply of the tRNA for the highly demanded codon, thus demonstrating that the codon usage of highly expressed genes was selected in evolution to maintain the efficiency of global protein translation.

The Codon Usage of Lowly Expressed Genes Is Subject to Natural Selection

Codon usage bias affects the genomes of organisms from all kingdoms of life and results from both background substitution biases and natural selection. Natural selection on codon usage to increase translation accuracy and efficiency has long been known to affect gene sequences. Such selection is stronger on highly, compared with lowly expressed genes, resulting in higher levels of codon bias within genes with higher expression levels. Additionally, selection on translation accuracy affects more strongly codons encoding conserved amino acids, since these will more often affect protein folding and/or function. By applying tests of selection on the gene sequences of the bacterium Escherichia coli, we demonstrate that both highly and lowly expressed genes display signals of selection on codon usage. Such signals are found for both conserved and less conserved amino acid positions, even within the 10% of E. coli genes expressed at the lowest levels. We further demonstrate experimentally that single synonymous codon replacements within a lowly expressed, essential gene can carry substantial effects on bacterial fitness. Combined, our results demonstrate that even within genes expressed at relatively low levels there is substantial selection on codon usage and that single synonymous codon replacements within such genes can have a marked effect on bacterial fitness.

Codon-Resolution Analysis Reveals a Direct and Context-Dependent Impact of Individual Synonymous Mutations on mRNA Level

Codon usage bias (CUB) refers to the observation that synonymous codons are not used equally frequently in a genome. CUB is stronger in more highly expressed genes, a phenomenon commonly explained by stronger natural selection on translational accuracy and/or efficiency among these genes. Nevertheless, this phenomenon could also occur if CUB regulates gene expression at the mRNA level, a hypothesis that has not been tested until recently. Here, we attempt to quantify the impact of synonymous mutations on mRNA level in yeast using 3,556 synonymous variants of a heterologous gene encoding green fluorescent protein (GFP) and 523 synonymous variants of an endogenous gene TDH3. We found that mRNA level was positively correlated with CUB among these synonymous variants, demonstrating a direct role of CUB in regulating transcript concentration, likely via regulating mRNA degradation rate, as our additional experiments suggested. More importantly, we quantified the effects of individual synonymous mutations on mRNA level and found them dependent on 1) CUB and 2) mRNA secondary structure, both in proximal sequence contexts. Our study reveals the pleiotropic effects of synonymous codon usage and provides an additional explanation for the well-known correlation between CUB and gene expression level.