stAIcalc: tRNA adaptation index calculator based on species-specific weights

Transfer RNA Levels Are Tuned to Support Differentiation During Drosophila Neurogenesis

BACKGROUND/OBJECTIVES

Neural differentiation requires a multifaceted program to alter gene expression along the proliferation to the differentiation axis. While critical changes occur at the level of transcription, post-transcriptional mechanisms allow fine-tuning of protein output. We investigated the role of tRNAs in regulating gene expression during neural differentiation in Drosophila larval brains.

METHODS

We quantified tRNA abundance in neural progenitor-biased and neuron-biased brains using the hydrotRNA-seq method. These tRNA data were combined with cell type-specific mRNA decay measurements and transcriptome profiles in order to model how tRNA abundance affects mRNA stability and translation efficiency.

RESULTS

We found that (1) tRNA abundance is largely constant between neural progenitors and neurons but significant variation exists for 10 nuclear tRNA genes and 8 corresponding anticodon groups, (2) tRNA abundance correlates with codon-mediated mRNA decay in neuroblasts and neurons, but does not completely explain the different stabilizing or destabilizing effects of certain codons, and (3) changes in tRNA levels support a shift in translation optimization from a program supporting proliferation to a program supporting differentiation.

CONCLUSIONS

These findings reveal coordination between tRNA expression and codon usage in transcripts that regulate neural development.

The dynamics and functional impact of tRNA repertoires during early embryogenesis in zebrafish

Embryogenesis entails dramatic shifts in mRNA translation and turnover that reprogram gene expression during cellular proliferation and differentiation. Codon identity modulates mRNA stability during early vertebrate embryogenesis, but how the composition of tRNA pools is matched to translational demand is unknown. By quantitative profiling of tRNA repertoires in zebrafish embryos during the maternal-to-zygotic transition, we show that zygotic tRNA repertoires are established after the onset of gastrulation, succeeding the major wave of zygotic mRNA transcription. Maternal and zygotic tRNA pools are distinct, but their reprogramming does not result in a better match to the codon content of the zygotic transcriptome. Instead, we find that an increase in global translation at gastrulation sensitizes decoding rates to tRNA supply, thus destabilizing maternal mRNAs enriched in slowly translated codons. Translational activation and zygotic tRNA expression temporally coincide with an increase of TORC1 activity at gastrulation, which phosphorylates and inactivates the RNA polymerase III repressor Maf1a/b. Our data indicate that a switch in global translation, rather than tRNA reprogramming, determines the onset of codon-dependent maternal mRNA decay during zebrafish embryogenesis.

SARS-CoV-2 Displays a Suboptimal Codon Usage Bias for Efficient Translation in Human Cells Diverted by Hijacking the tRNA Epitranscriptome

Codon bias analysis of SARS-CoV-2 reveals suboptimal adaptation for translation in human cells it infects. The detailed examination of the codons preferentially used by SARS-CoV-2 shows a strong preference for LysAAA, GlnCAA, GluGAA, and ArgAGA, which are infrequently used in human genes. In the absence of an adapted tRNA pool, efficient decoding of these codons requires a 5-methoxycarbonylmethyl-2-thiouridine (mcm5s2) modification at the U34 wobble position of the corresponding tRNAs (tLysUUU; tGlnUUG; tGluUUC; tArgUCU). The optimal translation of SARS-CoV-2 open reading frames (ORFs) may therefore require several adjustments to the host's translation machinery, enabling the highly biased viral genome to achieve a more favorable "Ready-to-Translate" state in human cells. Experimental approaches based on LC-MS/MS quantification of tRNA modifications and on alteration of enzymatic tRNA modification pathways provide strong evidence to support the hypothesis that SARS-CoV-2 induces U34 tRNA modifications and relies on these modifications for its lifecycle. The conclusions emphasize the need for future studies on the evolution of SARS-CoV-2 codon bias and its ability to alter the host tRNA pool through the manipulation of RNA modifications.

Link Between Individual Codon Frequencies and Protein Expression: Going Beyond Codon Adaptation Index

An important role of a particular synonymous codon composition of a gene in its expression level is well known. There are a number of algorithms optimizing codon usage of recombinant genes to maximize their expression in host cells. Nevertheless, the underlying mechanism remains unsolved and is of significant relevance. In the realm of modern biotechnology, directing protein production to a specific level is crucial for metabolic engineering, genome rewriting and a growing number of other applications. In this study, we propose two new simple statistical and empirical methods for predicting the protein expression level from the nucleotide sequence of the corresponding gene: Codon Expression Index Score (CEIS) and Codon Productivity Score (CPS). Both of these methods are based on the influence of each individual codon in the gene on the overall expression level of the encoded protein and the frequencies of isoacceptors in the species. Our predictions achieve a correlation level of up to r = 0.7 with experimentally measured quantitative proteome data of Escherichia coli, which is superior to any previously proposed methods. Our work helps understand how codons determine protein abundances. Based on these methods, it is possible to design proteins optimized for expression in a particular organism.

Codon-optimization in gene therapy: promises, prospects and challenges

Codon optimization has evolved to enhance protein expression efficiency by exploiting the genetic code's redundancy, allowing for multiple codon options for a single amino acid. Initially observed in E. coli, optimal codon usage correlates with high gene expression, which has propelled applications expanding from basic research to biopharmaceuticals and vaccine development. The method is especially valuable for adjusting immune responses in gene therapies and has the potenial to create tissue-specific therapies. However, challenges persist, such as the risk of unintended effects on protein function and the complexity of evaluating optimization effectiveness. Despite these issues, codon optimization is crucial in advancing gene therapeutics. This study provides a comprehensive review of the current metrics for codon-optimization, and its practical usage in research and clinical applications, in the context of gene therapy.

Specific codons control cellular resources and fitness

As cellular engineering progresses from simply overexpressing proteins to imparting complex phenotypes through multigene expression, judicious appropriation of cellular resources is essential. Since codon use is degenerate and biased, codons may control cellular resources at a translational level. We investigate how partitioning transfer RNA (tRNA) resources by incorporating dissimilar codon usage can drastically alter interdependence of expression level and burden on the host. By isolating the effect of individual codons' use during translation elongation while eliminating confounding factors, we show that codon choice can trans-regulate fitness of the host and expression of other heterologous or native genes. We correlate specific codon usage patterns with host fitness and derive a coding scheme for multigene expression called the Codon Health Index (CHI, χ). This empirically derived coding scheme (χ) enables the design of multigene expression systems that avoid catastrophic cellular burden and is robust across several proteins and conditions.

A spatiotemporally resolved atlas of mRNA decay in the C. elegans embryo reveals differential regulation of mRNA stability across stages and cell types

During embryonic development, cells undergo dynamic changes in gene expression that are required for appropriate cell fate specification. Although both transcription and mRNA degradation contribute to gene expression dynamics, patterns of mRNA decay are less well-understood. Here we directly measured spatiotemporally resolved mRNA decay rates transcriptome-wide throughout C. elegans embryogenesis by transcription inhibition followed by bulk and single-cell RNA-sequencing. This allowed us to calculate mRNA half-lives within specific cell types and developmental stages and identify differentially regulated mRNA decay throughout embryonic development. We identified transcript features that are correlated with mRNA stability and found that mRNA decay rates are associated with distinct peaks in gene expression over time. Moreover, we provide evidence that, on average, mRNA is more stable in the germline compared to in the soma and in later embryonic stages compared to in earlier stages. This work suggests that differential mRNA decay across cell states and time helps to shape developmental gene expression, and it provides a valuable resource for studies of mRNA turnover regulatory mechanisms.

Molecular bases for strong phenotypic effects of single synonymous codon substitutions in the E. coli ccdB toxin gene

BACKGROUND

Single synonymous codon mutations typically have only minor or no effects on gene function. Here, we estimate the effects on cell growth of ~ 200 single synonymous codon mutations in an operonic context by mutating almost all positions of ccdB, the 101-residue long cytotoxin of the ccdAB Toxin-Antitoxin (TA) operon to most degenerate codons. Phenotypes were assayed by transforming the mutant library into CcdB sensitive and resistant E. coli strains, isolating plasmid pools, and subjecting them to deep sequencing. Since autoregulation is a hallmark of TA operons, phenotypes obtained for ccdB synonymous mutants after transformation in a RelE toxin reporter strain followed by deep sequencing provided information on the amount of CcdAB complex formed.

RESULTS

Synonymous mutations in the N-terminal region involved in translation initiation showed the strongest non-neutral phenotypic effects. We observe an interplay of numerous factors, namely, location of the codon, codon usage, t-RNA abundance, formation of anti-Shine Dalgarno sequences, predicted transcript secondary structure, and evolutionary conservation in determining phenotypic effects of ccdB synonymous mutations. Incorporation of an N-terminal, hyperactive synonymous mutation, in the background of the single synonymous codon mutant library sufficiently increased translation initiation, such that mutational effects on either folding or termination of translation became more apparent. Introduction of putative pause sites not only affects the translational rate, but might also alter the folding kinetics of the protein in vivo.

CONCLUSION

In summary, the study provides novel insights into diverse mechanisms by which synonymous mutations modulate gene function. This information is useful in optimizing heterologous gene expression in E. coli and understanding the molecular bases for alteration in gene expression that arise due to synonymous mutations.

Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals

Recombinant biopharmaceuticals including antigens, antibodies, hormones, cytokines, single-chain variable fragments, and peptides have been used as vaccines, diagnostics and therapeutics. Plant molecular pharming is a robust platform that uses plants as an expression system to produce simple and complex recombinant biopharmaceuticals on a large scale. Plant system has several advantages over other host systems such as humanized expression, glycosylation, scalability, reduced risk of human or animal pathogenic contaminants, rapid and cost-effective production. Despite many advantages, the expression of recombinant proteins in plant system is hindered by some factors such as non-human post-translational modifications, protein misfolding, conformation changes and instability. Artificial intelligence (AI) plays a vital role in various fields of biotechnology and in the aspect of plant molecular pharming, a significant increase in yield and stability can be achieved with the intervention of AI-based multi-approach to overcome the hindrance factors. Current limitations of plant-based recombinant biopharmaceutical production can be circumvented with the aid of synthetic biology tools and AI algorithms in plant-based glycan engineering for protein folding, stability, viability, catalytic activity and organelle targeting. The AI models, including but not limited to, neural network, support vector machines, linear regression, Gaussian process and regressor ensemble, work by predicting the training and experimental data sets to design and validate the protein structures thereby optimizing properties such as thermostability, catalytic activity, antibody affinity, and protein folding. This review focuses on, integrating systems engineering approaches and AI-based machine learning and deep learning algorithms in protein engineering and host engineering to augment protein production in plant systems to meet the ever-expanding therapeutics market.

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

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

Diverse signatures of convergent evolution in cacti-associated yeasts

Many distantly related organisms have convergently evolved traits and lifestyles that enable them to live in similar ecological environments. However, the extent of phenotypic convergence evolving through the same or distinct genetic trajectories remains an open question. Here, we leverage a comprehensive dataset of genomic and phenotypic data from 1,049 yeast species in the subphylum Saccharomycotina (Kingdom Fungi, Phylum Ascomycota) to explore signatures of convergent evolution in cactophilic yeasts, ecological specialists associated with cacti. We inferred that the ecological association of yeasts with cacti arose independently ~17 times. Using machine-learning, we further found that cactophily can be predicted with 76% accuracy from functional genomic and phenotypic data. The most informative feature for predicting cactophily was thermotolerance, which is likely associated with duplication and altered evolutionary rates of genes impacting the cell envelope in several cactophilic lineages. We also identified horizontal gene transfer and duplication events of plant cell wall-degrading enzymes in distantly related cactophilic clades, suggesting that putatively adaptive traits evolved through disparate molecular mechanisms. Remarkably, multiple cactophilic lineages and their close relatives are emerging human opportunistic pathogens, suggesting that the cactophilic lifestyle-and perhaps more generally lifestyles favoring thermotolerance-may preadapt yeasts to cause human disease. This work underscores the potential of a multifaceted approach involving high throughput genomic and phenotypic data to shed light onto ecological adaptation and highlights how convergent evolution to wild environments could facilitate the transition to human pathogenicity.

In silico methods for predicting functional synonymous variants

Single nucleotide variants (SNVs) contribute to human genomic diversity. Synonymous SNVs are previously considered to be "silent," but mounting evidence has revealed that these variants can cause RNA and protein changes and are implicated in over 85 human diseases and cancers. Recent improvements in computational platforms have led to the development of numerous machine-learning tools, which can be used to advance synonymous SNV research. In this review, we discuss tools that should be used to investigate synonymous variants. We provide supportive examples from seminal studies that demonstrate how these tools have driven new discoveries of functional synonymous SNVs.

Genes enriched in A/T-ending codons are co-regulated and conserved across mammals

Codon usage influences gene expression distinctly depending on the cell context. Yet, the importance of codon bias in the simultaneous turnover of specific groups of protein-coding genes remains to be investigated. Here, we find that genes enriched in A/T-ending codons are expressed more coordinately in general and across tissues and development than those enriched in G/C-ending codons. tRNA abundance measurements indicate that this coordination is linked to the expression changes of tRNA isoacceptors reading A/T-ending codons. Genes with similar codon composition are more likely to be part of the same protein complex, especially for genes with A/T-ending codons. The codon preferences of genes with A/T-ending codons are conserved among mammals and other vertebrates. We suggest that this orchestration contributes to tissue-specific and ontogenetic-specific expression, which can facilitate, for instance, timely protein complex formation.

ThrRS-Mediated Translation Regulation of the RNA Polymerase III Subunit RPC10 Occurs through an Element with Similarity to Cognate tRNA ASL and Affects tRNA Levels

Aminoacyl tRNA synthetases (aaRSs) are a well-studied family of enzymes with a canonical role in charging tRNAs with a specific amino acid. These proteins appear to also have non-canonical roles, including post-transcriptional regulation of mRNA expression. Many aaRSs were found to bind mRNAs and regulate their translation into proteins. However, the mRNA targets, mechanism of interaction, and regulatory consequences of this binding are not fully resolved. Here, we focused on yeast cytosolic threonine tRNA synthetase (ThrRS) to decipher its impact on mRNA binding. Affinity purification of ThrRS with its associated mRNAs followed by transcriptome analysis revealed a preference for mRNAs encoding RNA polymerase subunits. An mRNA that was significantly bound compared to all others was the mRNA encoding RPC10, a small subunit of RNA polymerase III. Structural modeling suggested that this mRNA includes a stem-loop element that is similar to the anti-codon stem loop (ASL) structure of ThrRS cognate tRNA (tRNAThr). We introduced random mutations within this element and found that almost every change from the normal sequence leads to reduced binding by ThrRS. Furthermore, point mutations at six key positions that abolish the predicted ASL-like structure showed a significant decrease in ThrRS binding with a decrease in RPC10 protein levels. Concomitantly, tRNAThr levels were reduced in the mutated strain. These data suggest a novel regulatory mechanism in which cellular tRNA levels are regulated through a mimicking element within an RNA polymerase III subunit in a manner that involves the tRNA cognate aaRS.

Codon optimality has minimal effect on determining translation efficiency in mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb) is a slow-growing, intracellular pathogen that exhibits a high GC-rich genome. Several factors, including the GC content of the genome, influence the evolution of specific codon usage biases in genomes. As a result, the Mtb genome exhibits strong biases for amino acid usage and codon usage. Codon usage of mRNAs affects several aspects of translation, including accuracy, efficiency, and protein folding. Here we address the effect of codon usage biases in determining the translation efficiency of mRNAs in Mtb. Unlike most commonly studied organisms, Mtb carries a single copy of each tRNA gene. However, we show that the relative levels of tRNAs in the Mtb tRNA pool vary by an order of magnitude. Our results show that the codons decoded by the abundant tRNAs indeed show higher adaptability. Moreover, there is a general positive correlation between genomic codon usage and the tRNA adaptability of codons (TAc). We further estimated the optimality of the codon and mRNAs by considering both the TAc and the tRNA demand. These measures did not show any correlation with mRNA abundance and translation efficiency. There was no correlation between tRNA adaptability and ribosome pausing as well. Taken together, we conclude that the translation machinery, and the tRNA pool of an organism, co-evolve with the codon usage to optimize the translation efficiency of an organism. Thus the deleterious effect of maladapted codons is not pronounced.

Selection for Translational Efficiency in Genes Associated with Alphaproteobacterial Gene Transfer Agents

Gene transfer agents (GTAs) are virus-like elements that are encoded by some bacterial and archaeal genomes. The production of GTAs can be induced by carbon depletion and results in host lysis and the release of virus-like particles that contain mostly random fragments of the host DNA. The remaining members of a GTA-producing population act as GTA recipients by producing proteins needed for GTA-mediated DNA acquisition. Here, we detected a codon usage bias toward codons with more readily available tRNAs in the RcGTA-like GTA genes of alphaproteobacterial genomes. Such bias likely improves the translational efficacy during GTA gene expression. While the strength of codon usage bias fluctuates substantially among individual GTA genes and across taxonomic groups, it is especially pronounced in Sphingomonadales, whose members are known to inhabit nutrient-depleted environments. By screening genomes for gene families with trends in codon usage biases similar to those in GTA genes, we found a gene that likely encodes head completion protein in some GTAs where it appeared missing, and 13 genes previously not implicated in the GTA life cycle. The latter genes are involved in various molecular processes, including the homologous recombination and transport of scarce organic matter. Our findings provide insights into the role of selection for translational efficiency in the evolution of GTA genes and outline genes that are potentially involved in the previously hypothesized integration of GTA-delivered DNA into the host genome. IMPORTANCE Horizontal gene transfer (HGT) is a fundamental process that drives evolution of microorganisms. HGT can result in a rapid dissemination of beneficial genes within and among microbial communities and can be achieved via multiple mechanisms. One peculiar HGT mechanism involves viruses "domesticated" by some bacteria and archaea (their hosts). These so-called gene transfer agents (GTAs) are encoded in hosts' genomes, produced under starvation conditions, and cannot propagate themselves as viruses. We show that GTA genes are under selection to improve the efficiency of their translation when the host activates GTA production. The selection is especially pronounced in bacteria that occupy nutrient-depleted environments. Intriguingly, several genes involved in incorporation of DNA into a genome are under similar selection pressure, suggesting that they may facilitate the integration of GTA-delivered DNA into the host genome. Our findings underscore the potential importance of GTAs as a mechanism of HGT under nutrient-limited conditions, which are widespread in microbial habitats.

A large-scale analysis of codon usage bias in 4868 bacterial genomes shows association of codon adaptation index with GC content, protein functional domains and bacterial phenotypes

Multiple synonymous codons code for the same amino acid, resulting in the degeneracy of the genetic code and in the preferred used of some codons called codon bias usage (CBU). We performed a large-scale analysis of codon usage bias analysing the distribution of the codon adaptation index (CAI) and the codon relative adaptiveness index (RA) in 4868 bacterial genomes. We found that CAI values differ significantly between protein functional domains and part of the protein outside domains and show how CAI, GC content and preferred usage of polymerase III alpha subunits are related. Additionally, we give evidence of the association between CAI and bacterial phenotypes.

Evolution and host adaptability of plant RNA viruses: Research insights on compositional biases

During recent decades, many new emerging or re-emerging RNA viruses have been found in plants through the development of deep-sequencing technology and big data analysis. These findings largely changed our understanding of the origin, evolution and host range of plant RNA viruses. There is evidence that their genetic composition originates from viruses, and host populations play a key role in the evolution and host adaptability of plant RNA viruses. In this mini-review, we describe the state of our understanding of the evolution of plant RNA viruses in view of compositional biases and explore how they adapt to the host. It appears that adenine rich (A-rich) coding sequences, low CpG and UpA dinucleotide frequencies and lower codon usage patterns were found in the vast majority of plant RNA viruses. The codon usage pattern of plant RNA viruses was influenced by both natural selection and mutation pressure, and natural selection mostly from hosts was the dominant factor. The codon adaptation analyses support that plant RNA viruses probably evolved a dynamic balance between codon adaptation and deoptimization to maintain efficient replication cycles in multiple hosts with various codon usage patterns. In the future, additional combinations of computational and experimental analyses of the nucleotide composition and codon usage of plant RNA viruses should be addressed.

Expression level is a major modifier of the fitness landscape of a protein coding gene

The phenotypic consequence of a genetic mutation depends on many factors including the expression level of a gene. However, a comprehensive quantification of this expression effect is still lacking, as is a further general mechanistic understanding of the effect. Here, we measured the fitness effect of almost all (>97.5%) single-nucleotide mutations in GFP, an exogenous gene with no physiological function, and URA3, a conditionally essential gene. Both genes were driven by two promoters whose expression levels differed by around tenfold. The resulting fitness landscapes revealed that the fitness effects of at least 42% of all single-nucleotide mutations within the genes were expression dependent. Although only a small fraction of variation in fitness effects among different mutations can be explained by biophysical properties of the protein and messenger RNA of the gene, our analyses revealed that the avoidance of stochastic molecular errors generally underlies the expression dependency of mutational effects and suggested protein misfolding as the most important type of molecular error among those examined. Our results therefore directly explained the slower evolution of highly expressed genes and highlighted cytotoxicity due to stochastic molecular errors as a non-negligible component for understanding the phenotypic consequence of mutations.

Protein Abundance Prediction Through Machine Learning Methods

Proteins are responsible for most physiological processes, and their abundance provides crucial information for systems biology research. However, absolute protein quantification, as determined by mass spectrometry, still has limitations in capturing the protein pool. Protein abundance is impacted by translation kinetics, which rely on features of codons. In this study, we evaluated the effect of codon usage bias of genes on protein abundance. Notably, we observed differences regarding codon usage patterns between genes coding for highly abundant proteins and genes coding for less abundant proteins. Analysis of synonymous codon usage and evolutionary selection showed a clear split between the two groups. Our machine learning models predicted protein abundances from codon usage metrics with remarkable accuracy, achieving strong correlation with experimental data. Upon integration of the predicted protein abundance in enzyme-constrained genome-scale metabolic models, the simulated phenotypes closely matched experimental data, which demonstrates that our predictive models are valuable tools for systems metabolic engineering approaches.

tRNA copy number and codon usage in the sea cucumber genome provide insights into adaptive translation for saponin biosynthesis

Genomic tRNA copy numbers determine cytoplasmic tRNA abundances, which in turn influence translation efficiency, but the underlying mechanism is not well understood. Using the sea cucumber Apostichopus japonicus as a model, we combined genomic sequence, transcriptome expression and ecological food resource data to study its codon usage adaptation. The results showed that, unlike intragenic non-coding RNAs, transfer RNAs (tRNAs) tended to be transcribed independently. This may be attributed to their specific Pol III promoters that lack transcriptional regulation, which may underlie the correlation between genomic copy number and cytoplasmic abundance of tRNAs. Moreover, codon usage optimization was mostly restrained by a gene's amino acid sequence, which might be a compromise between functionality and translation efficiency for stress responses were highly optimized for most echinoderms, while enzymes for saponin biosynthesis (LAS, CYPs and UGTs) were especially optimized in sea cucumbers, which might promote saponin synthesis as a defence strategy. The genomic tRNA content of A. japonicus was positively correlated with amino acid content in its natural food particles, which should promote its efficiency in protein synthesis. We propose that coevolution between genomic tRNA content and codon usage of sea cucumbers facilitates their saponin synthesis and survival using food resources with low nutrient content.

Codon usage and protein length-dependent feedback from translation elongation regulates translation initiation and elongation speed

Essential cellular functions require efficient production of many large proteins but synthesis of large proteins encounters many obstacles in cells. Translational control is mostly known to be regulated at the initiation step. Whether translation elongation process can feedback to regulate initiation efficiency is unclear. Codon usage bias, a universal feature of all genomes, plays an important role in determining gene expression levels. Here, we discovered that there is a conserved but codon usage-dependent genome-wide negative correlation between protein abundance and CDS length. The codon usage effects on protein expression and ribosome flux on mRNAs are influenced by CDS length; optimal codon usage preferentially promotes production of large proteins. Translation of mRNAs with long CDS and non-optimal codon usage preferentially induces phosphorylation of initiation factor eIF2α, which inhibits translation initiation efficiency. Deletion of the eIF2α kinase CPC-3 (GCN2 homolog) in Neurospora preferentially up-regulates large proteins encoded by non-optimal codons. Surprisingly, CPC-3 also inhibits translation elongation rate in a codon usage and CDS length-dependent manner, resulting in slow elongation rates for long CDS mRNAs. Together, these results revealed a codon usage and CDS length-dependent feedback mechanism from translation elongation to regulate both translation initiation and elongation kinetics.

Evolutionary responses to codon usage of horizontally transferred genes in Pseudomonas aeruginosa: gene retention, amelioration and compensatory evolution

Prokaryote genome evolution is characterized by the frequent gain of genes through horizontal gene transfer (HGT). For a gene, being horizontally transferred can represent a strong change in its genomic and physiological context. If the codon usage of a transferred gene deviates from that of the receiving organism, the fitness benefits it provides can be reduced due to a mismatch with the expression machinery. Consequently, transferred genes with a deviating codon usage can be selected against or elicit evolutionary responses that enhance their integration, such as gene amelioration and compensatory evolution. Within bacterial species, the extent and relative importance of these different mechanisms has never been considered altogether. In this study, a phylogeny-based method was used to investigate the occurrence of these different evolutionary responses in Pseudomonas aeruginosa. Selection on codon usage of genes acquired through HGT was observed over evolutionary time, with the overall codon usage converging towards that of the core genome. Gene amelioration, through the accumulation of synonymous mutations after HGT, did not seem to systematically affect transferred genes. This pattern therefore seemed to be mainly driven by selective retention of transferred genes with an initial codon usage similar to that of the core genes. Additionally, variation in the copy number of tRNA genes was often associated with the acquisition of genes for which the observed variation could enhance their expression. This provides evidence that compensatory evolution might be an important mechanism for the integration of horizontally transferred genes.

Codon-based indices for modeling gene expression and transcript evolution

Codon usage bias (CUB) refers to the phenomena that synonymous codons are used in different frequencies in most genes and organisms. The general assumption is that codon biases reflect a balance between mutational biases and natural selection. Today we understand that the codon content is related and can affect all gene expression steps. Starting from the 1980s, codon-based indices have been used for answering different questions in all biomedical fields, including systems biology, agriculture, medicine, and biotechnology. In general, codon usage bias indices weigh each codon or a small set of codons to estimate the fitting of a certain coding sequence to a certain phenomenon (e.g., bias in codons, adaptation to the tRNA pool, frequencies of certain codons, transcription elongation speed, etc.) and are usually easy to implement. Today there are dozens of such indices; thus, this paper aims to review and compare the different codon usage bias indices, their applications, and advantages. In addition, we perform analysis that demonstrates that most indices tend to correlate even though they aim to capture different aspects. Due to the centrality of codon usage bias on different gene expression steps, it is important to keep developing new indices that can capture additional aspects that are not modeled with the current indices.

Signatures of optimal codon usage in metabolic genes inform budding yeast ecology

Reverse ecology is the inference of ecological information from patterns of genomic variation. One rich, heretofore underutilized, source of ecologically relevant genomic information is codon optimality or adaptation. Bias toward codons that match the tRNA pool is robustly associated with high gene expression in diverse organisms, suggesting that codon optimization could be used in a reverse ecology framework to identify highly expressed, ecologically relevant genes. To test this hypothesis, we examined the relationship between optimal codon usage in the classic galactose metabolism (GAL) pathway and known ecological niches for 329 species of budding yeasts, a diverse subphylum of fungi. We find that optimal codon usage in the GAL pathway is positively correlated with quantitative growth on galactose, suggesting that GAL codon optimization reflects increased capacity to grow on galactose. Optimal codon usage in the GAL pathway is also positively correlated with human-associated ecological niches in yeasts of the CUG-Ser1 clade and with dairy-associated ecological niches in the family Saccharomycetaceae. For example, optimal codon usage of GAL genes is greater than 85% of all genes in the genome of the major human pathogen Candida albicans (CUG-Ser1 clade) and greater than 75% of genes in the genome of the dairy yeast Kluyveromyces lactis (family Saccharomycetaceae). We further find a correlation between optimization in the GALactose pathway genes and several genes associated with nutrient sensing and metabolism. This work suggests that codon optimization harbors information about the metabolic ecology of microbial eukaryotes. This information may be particularly useful for studying fungal dark matter-species that have yet to be cultured in the lab or have only been identified by genomic material.

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.

Structural and Genetic Determinants of Convergence in the Drosophila tRNA Structure-Function Map

The evolution of tRNA multigene families remains poorly understood, exhibiting unusual phenomena such as functional conversions of tRNA genes through anticodon shift substitutions. We improved FlyBase tRNA gene annotations from twelve Drosophila species, incorporating previously identified ortholog sets to compare substitution rates across tRNA bodies at single-site and base-pair resolution. All rapidly evolving sites fell within the same metal ion-binding pocket that lies at the interface of the two major stacked helical domains. We applied our tRNA Structure-Function Mapper (tSFM) method independently to each Drosophila species and one outgroup species Musca domestica and found that, although predicted tRNA structure-function maps are generally highly conserved in flies, one tRNA Class-Informative Feature (CIF) within the rapidly evolving ion-binding pocket-Cytosine 17 (C17), ancestrally informative for lysylation identity-independently gained asparaginylation identity and substituted in parallel across tRNAAsn paralogs at least once, possibly multiple times, during evolution of the genus. In D. melanogaster, most tRNALys and tRNAAsn genes are co-arrayed in one large heterologous gene cluster, suggesting that heterologous gene conversion as well as structural similarities of tRNA-binding interfaces in the closely related asparaginyl-tRNA synthetase (AsnRS) and lysyl-tRNA synthetase (LysRS) proteins may have played a role in these changes. A previously identified Asn-to-Lys anticodon shift substitution in D. ananassae may have arisen to compensate for the convergent and parallel gains of C17 in tRNAAsn paralogs in that lineage. Our results underscore the functional and evolutionary relevance of our tRNA structure-function map predictions and illuminate multiple genomic and structural factors contributing to rapid, parallel and compensatory evolution of tRNA multigene families.

In silico mutagenesis of human ACE2 with S protein and translational efficiency explain SARS-CoV-2 infectivity in different species

The coronavirus disease COVID-19 constitutes the most severe pandemic of the last decades having caused more than 1 million deaths worldwide. The SARS-CoV-2 virus recognizes the angiotensin converting enzyme 2 (ACE2) on the surface of human cells through its spike protein. It has been reported that the coronavirus can mildly infect cats, and ferrets, and perhaps dogs while not pigs, mice, chicken and ducks. Differences in viral infectivity among different species or individuals could be due to amino acid differences at key positions of the host proteins that interact with the virus, the immune response, expression levels of host proteins and translation efficiency of the viral proteins among other factors. Here, first we have addressed the importance that sequence variants of different animal species, human individuals and virus isolates have on the interaction between the RBD domain of the SARS-CoV-2 spike S protein and human angiotensin converting enzyme 2 (ACE2). Second, we have looked at viral translation efficiency by using the tRNA adaptation index. We find that integration of both interaction energy with ACE2 and translational efficiency explains animal infectivity. Humans are the top species in which SARS-CoV-2 is both efficiently translated as well as optimally interacting with ACE2. We have found some viral mutations that increase affinity for hACE and some hACE2 variants affecting ACE2 stability and virus binding. These variants suggest that different sensitivities to coronavirus infection in humans could arise in some cases from allelic variability affecting ACE2 stability and virus binding.

RiboMiner: a toolset for mining multi-dimensional features of the translatome with ribosome profiling data

Background

Ribosome profiling has been widely used for studies of translation under a large variety of cellular and physiological contexts. Many of these studies have greatly benefitted from a series of data-mining tools designed for dissection of the translatome from different aspects. However, as the studies of translation advance quickly, the current toolbox still falls in short, and more specialized tools are in urgent need for deeper and more efficient mining of the important and new features of the translation landscapes.

Results

Here, we present RiboMiner, a bioinformatics toolset for mining of multi-dimensional features of the translatome with ribosome profiling data. RiboMiner performs extensive quality assessment of the data and integrates a spectrum of tools for various metagene analyses of the ribosome footprints and for detailed analyses of multiple features related to translation regulation. Visualizations of all the results are available. Many of these analyses have not been provided by previous methods. RiboMiner is highly flexible, as the pipeline could be easily adapted and customized for different scopes and targets of the studies.

Conclusions

Applications of RiboMiner on two published datasets did not only reproduced the main results reported before, but also generated novel insights into the translation regulation processes. Therefore, being complementary to the current tools, RiboMiner could be a valuable resource for dissections of the translation landscapes and the translation regulations by mining the ribosome profiling data more comprehensively and with higher resolution. RiboMiner is freely available at https://github.com/xryanglab/RiboMiner and https://pypi.org/project/RiboMiner.

Adaptation of codon usage to tRNA I34 modification controls translation kinetics and proteome landscape

Codon usage bias is a universal feature of all genomes and plays an important role in regulating protein expression levels. Modification of adenosine to inosine at the tRNA anticodon wobble position (I34) by adenosine deaminases (ADATs) is observed in all eukaryotes and has been proposed to explain the correlation between codon usage and tRNA pool. However, how the tRNA pool is affected by I34 modification to influence codon usage-dependent gene expression is unclear. Using Neurospora crassa as a model system, by combining molecular, biochemical and bioinformatics analyses, we show that silencing of adat2 expression severely impaired the I34 modification levels for the ADAT-related tRNAs, resulting in major ADAT-related tRNA profile changes and reprogramming of translation elongation kinetics on ADAT-related codons. adat2 silencing also caused genome-wide codon usage-biased ribosome pausing on mRNAs and proteome landscape changes, leading to selective translational repression or induction of different mRNAs. The induced expression of CPC-1, the Neurospora ortholog of yeast GCN4p, mediates the transcriptional response after adat2 silencing and amino acid starvation. Together, our results demonstrate that the tRNA I34 modification by ADAT plays a major role in driving codon usage-biased translation to shape proteome landscape.

Modification of transfer RNA (tRNA) can have profound impacts on gene expression by shaping cellular tRNA pool. How codon usage bias and tRNA profiles synergistically regulate gene expression is unclear. By combining molecular, biochemical and bioinformatics analyses, we showed that the correlation between genome codon usage and tRNA I34 (inosine 34) modification modulates translation elongation kinetics and proteome landscape. Inhibition of tRNA I34 modification causes codon usage-dependent ribosome pausing on mRNAs during translation and changes cellular protein contents in a codon usage biased manner. Together, our results demonstrate that the tRNA I34 modification plays a major role in driving codon usage-dependent translation to determine proteome landscape in a eukaryotic organism.

Modelling and measuring intracellular competition for finite resources during gene expression

Dissecting the competition between genes for shared expressional resources is of fundamental importance for understanding the interplay between cellular components. Owing to the relationship between gene expression and cellular fitness, genomes are shaped by evolution to improve resource allocation. Whereas experimental approaches to investigate intracellular competition require technical resources and human expertise, computational models and in silico simulations allow vast numbers of experiments to be carried out and controlled easily, and with significantly reduced costs. Thus, modelling competition has a pivotal role in understanding the effects of competition on the biophysics of the cell. In this article, we review various computational models proposed to describe the different types of competition during gene expression. We also present relevant synthetic biology experiments and their biotechnological implications, and discuss the open questions in the field.

BR-Bodies Provide Selectively Permeable Condensates that Stimulate mRNA Decay and Prevent Release of Decay Intermediates

Biomolecular condensates play a key role in organizing RNAs and proteins into membraneless organelles. Bacterial RNP-bodies (BR-bodies) are a biomolecular condensate containing the RNA degradosome mRNA decay machinery, but the biochemical function of such organization remains poorly defined. Here, we define the RNA substrates of BR-bodies through enrichment of the bodies followed by RNA sequencing (RNA-seq). We find that long, poorly translated mRNAs, small RNAs, and antisense RNAs are the main substrates, while rRNA, tRNA, and other conserved non-coding RNAs (ncRNAs) are excluded from these bodies. BR-bodies stimulate the mRNA decay rate of enriched mRNAs, helping to reshape the cellular mRNA pool. We also observe that BR-body formation promotes complete mRNA decay, avoiding the buildup of toxic endo-cleaved mRNA decay intermediates. The combined selective permeability of BR-bodies for both enzymes and substrates together with the stimulation of the sub-steps of mRNA decay provide an effective organization strategy for bacterial mRNA decay.

The Critical Role of Codon Composition on the Translation Efficiency Robustness of the Hepatitis A Virus Capsid

Hepatoviruses show an intriguing deviated codon usage, suggesting an evolutionary signature. Abundant and rare codons in the cellular genome are scarce in the human hepatitis A virus (HAV) genome, while intermediately abundant host codons are abundant in the virus. Genotype–phenotype maps, or fitness landscapes, are a means of representing a genotype position in sequence space and uncovering how genotype relates to phenotype and fitness. Using genotype–phenotype maps of the translation efficiency, we have shown the critical role of the HAV capsid codon composition in regulating translation and determining its robustness. Adaptation to an environmental perturbation such as the artificial induction of cellular shutoff—not naturally occurring in HAV infection—involved movements in the sequence space and dramatic changes of the translation efficiency. Capsid rare codons, including abundant and rare codons of the cellular genome, slowed down the translation efficiency in conditions of no cellular shutoff. In contrast, rare capsid codons that are abundant in the cellular genome were efficiently translated in conditions of shutoff. Capsid regions very rich in slowly translated codons adapt to shutoff through sequence space movements from positions with highly robust translation to others with diminished translation robustness. These movements paralleled decreases of the capsid physical and biological robustness, and resulted in the diversification of capsid phenotypes. The deviated codon usage of extant hepatoviruses compared with that of their hosts may suggest the occurrence of a virus ancestor with an optimized codon usage with respect to an unknown ancient host.

Uncovering extensive post-translation regulation during human cell cycle progression by integrative multi-'omics analysis

BACKGROUND

Analysis of high-throughput multi-'omics interactions across the hierarchy of expression has wide interest in making inferences with regard to biological function and biomarker discovery. Expression levels across different scales are determined by robust synthesis, regulation and degradation processes, and hence transcript (mRNA) measurements made by microarray/RNA-Seq only show modest correlation with corresponding protein levels.

RESULTS

In this work we are interested in quantitative modelling of correlation across such gene products. Building on recent work, we develop computational models spanning transcript, translation and protein levels at different stages of the H. sapiens cell cycle. We enhance this analysis by incorporating 25+ sequence-derived features which are likely determinants of cellular protein concentration and quantitatively select for relevant features, producing a vast dataset with thousands of genes. We reveal insights into the complex interplay between expression levels across time, using machine learning methods to highlight outliers with respect to such models as proteins associated with post-translationally regulated modes of action.

CONCLUSIONS

We uncover quantitative separation between modified and degraded proteins that have roles in cell cycle regulation, chromatin remodelling and protein catabolism according to Gene Ontology; and highlight the opportunities for providing biological insights in future model systems.

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.

ExtRamp: a novel algorithm for extracting the ramp sequence based on the tRNA adaptation index or relative codon adaptiveness

Different species, genes, and locations within genes use different codons to fine-tune gene expression. Within genes, the ramp sequence assists in ribosome spacing and decreases downstream collisions by incorporating slowly-translated codons at the beginning of a gene. Although previously reported as occurring in some species, no previous attempt at extracting the ramp sequence from specific genes has been published. We present ExtRamp, a software package that quickly extracts ramp sequences from any species using the tRNA adaptation index or relative codon adaptiveness. Different filters facilitate the analysis of codon efficiency and enable identification of genes with a ramp sequence. We validate the existence of a ramp sequence in most species by running ExtRamp on 229 742 339 genes across 23 428 species. We evaluate differences in reported ramp sequences when we use different parameters. Using the strictest ramp sequence cut-off, we show that across most taxonomic groups, ramp sequences are approximately 20–40 codons long and occur in about 10% of gene sequences. We also show that in Drosophila melanogaster as gene expression increases, a higher proportion of genes have ramp sequences. We provide a framework for performing this analysis on other species. ExtRamp is freely available at https://github.com/ridgelab/ExtRamp.

The distribution of fitness effects among synonymous mutations in a gene under directional selection

The fitness effects of synonymous mutations, nucleotide changes that do not alter the encoded amino acid, have often been assumed to be neutral, but a growing body of evidence suggests otherwise. We used site-directed mutagenesis coupled with direct measures of competitive fitness to estimate the distribution of fitness effects among synonymous mutations for a gene under directional selection and capable of adapting via synonymous nucleotide changes. Synonymous mutations had highly variable fitness effects, both deleterious and beneficial, resembling those of nonsynonymous mutations in the same gene. This variation in fitness was underlain by changes in transcription linked to the creation of internal promoter sites. A positive correlation between fitness and the presence of synonymous substitutions across a phylogeny of related Pseudomonads suggests these mutations may be common in nature. Taken together, our results provide the most compelling evidence to date that synonymous mutations with non-neutral fitness effects may in fact be commonplace.

Networks of ribosome flow models for modeling and analyzing intracellular traffic

The ribosome flow model with input and output (RFMIO) is a deterministic dynamical system that has been used to study the flow of ribosomes during mRNA translation. The input of the RFMIO controls its initiation rate and the output represents the ribosome exit rate (and thus the protein production rate) at the 3′ end of the mRNA molecule. The RFMIO and its variants encapsulate important properties that are relevant to modeling ribosome flow such as the possible evolution of “traffic jams” and non-homogeneous elongation rates along the mRNA molecule, and can also be used for studying additional intracellular processes such as transcription, transport, and more. Here we consider networks of interconnected RFMIOs as a fundamental tool for modeling, analyzing and re-engineering the complex mechanisms of protein production. In these networks, the output of each RFMIO may be divided, using connection weights, between several inputs of other RFMIOs. We show that under quite general feedback connections the network has two important properties: (1) it admits a unique steady-state and every trajectory converges to this steady-state; and (2) the problem of how to determine the connection weights so that the network steady-state output is maximized is a convex optimization problem. These mathematical properties make these networks highly suitable as models of various phenomena: property (1) means that the behavior is predictable and ordered, and property (2) means that determining the optimal weights is numerically tractable even for large-scale networks. For the specific case of a feed-forward network of RFMIOs we prove an additional useful property, namely, that there exists a spectral representation for the network steady-state, and thus it can be determined without any numerical simulations of the dynamics. We describe the implications of these results to several fundamental biological phenomena and biotechnological objectives.

STADIUM: Species-Specific tRNA Adaptive Index Compendium

Due to the increasing interest in synonymous codons, several codon bias-related terms were introduced. As one measure of them, the tRNA adaptation index (tAI) was invented about a decade ago. The tAI is a measure of translational efficiency for a gene and is calculated based on the abundance of intracellular tRNA and the binding strength between a codon and a tRNA. The index has been widely used in various fields of molecular evolution, genetics, and pharmacology. Afterwards, an improved version of the index, named specific tRNA adaptation index (stAI), was developed by adapting tRNA copy numbers in species. Although a subsequently developed webserver (stAIcalc) provided tools that calculated stAI values, it was not available to access pre-calculated values. In addition to about 100 species in stAIcalc, we calculated stAI values for whole coding sequences in 148 species. To enable easy access to this index, we constructed a novel web database, named STADIUM (Species-specific tRNA adaptive index compendium). STADIUM provides not only the stAI value of each gene but also statistics based on pathway-based classification. The database is expected to help researchers who have interests in codon optimality and the role of synonymous codons. STADIUM is freely available at http://stadium.pmrc.re.kr.

Tuning recombinant protein expression to match secretion capacity

Background

The secretion of recombinant disulfide-bond containing proteins into the periplasm of Gram-negative bacterial hosts, such as E. coli, has many advantages that can facilitate product isolation, quality and activity. However, the secretion machinery of E. coli has a limited capacity and can become overloaded, leading to cytoplasmic retention of product; which can negatively impact cell viability and biomass accumulation. Fine control over recombinant gene expression offers the potential to avoid this overload by matching expression levels to the host secretion capacity.

Results

Here we report the application of the RiboTite gene expression control system to achieve this by finely controlling cellular expression levels. The level of control afforded by this system allows cell viability to be maintained, permitting production of high-quality, active product with enhanced volumetric titres.

Conclusions

The methods and systems reported expand the tools available for the production of disulfide-bond containing proteins, including antibody fragments, in bacterial hosts.

Electronic supplementary material

The online version of this article (10.1186/s12934-018-1047-z) contains supplementary material, which is available to authorized users.

Evolution of Codon Usage Bias in Henipaviruses Is Governed by Natural Selection and Is Host-Specific

Hendra virus (HeV) and Nipah virus (NiV) are among a group of emerging bat-borne paramyxoviruses that have crossed their species-barrier several times by infecting several hosts with a high fatality rate in human beings. Despite the fatal nature of their infection, a comprehensive study to explore their evolution and adaptation in different hosts is lacking. A study of codon usage patterns in henipaviruses may provide some fruitful insight into their evolutionary processes of synonymous codon usage and host-adapted evolution. Here, we performed a systematic evolutionary and codon usage bias analysis of henipaviruses. We found a low codon usage bias in the coding sequences of henipaviruses and that natural selection, mutation pressure, and nucleotide compositions shapes the codon usage patterns of henipaviruses, with natural selection being more important than the others. Also, henipaviruses showed the highest level of adaptation to bats of the genus Pteropus in the codon adaptation index (CAI), relative to the codon de-optimization index (RCDI), and similarity index (SiD) analyses. Furthermore, a comparison to recently identified henipa-like viruses indicated a high tRNA adaptation index of henipaviruses for human beings, mainly due to F, G and L proteins. Consequently, the study concedes the substantial emergence of henipaviruses in human beings, particularly when paired with frequent exposure to direct/indirect bat excretions.

Roadblocks and resolutions in eukaryotic translation

During protein synthesis, ribosomes encounter many roadblocks, the outcomes of which are largely determined by substrate availability, amino acid features and reaction kinetics. Prolonged ribosome stalling is likely to be resolved by ribosome rescue or quality control pathways, whereas shorter stalling is likely to be resolved by ongoing productive translation. How ribosome function is affected by such hindrances can therefore have a profound impact on the translational output (yield) of a particular mRNA. In this Review, we focus on these roadblocks and the resumption of normal translation elongation rather than on alternative fates wherein the stalled ribosome triggers degradation of the mRNA and the incomplete protein product. We discuss the fundamental stages of the translation process in eukaryotes, from elongation through ribosome recycling, with particular attention to recent discoveries of the complexity of the genetic code and regulatory elements that control gene expression, including ribosome stalling during elongation, the role of mRNA context in translation termination and mechanisms of ribosome rescue that resemble recycling.

Decoupling the impact of microRNAs on translational repression versus RNA degradation in embryonic stem cells

Translation and mRNA degradation are intimately connected, yet the mechanisms that link them are not fully understood. Here, we studied these mechanisms in embryonic stem cells (ESCs). Transcripts showed a wide range of stabilities, which correlated with their relative translation levels and that did not change during early ESC differentiation. The protein DHH1 links translation to mRNA stability in yeast; however, loss of the mammalian homolog, DDX6, in ESCs did not disrupt the correlation across transcripts. Instead, the loss of DDX6 led to upregulated translation of microRNA targets, without concurrent changes in mRNA stability. The Ddx6 knockout cells were phenotypically and molecularly similar to cells lacking all microRNAs (Dgcr8 knockout ESCs). These data show that the loss of DDX6 can separate the two canonical functions of microRNAs: translational repression and transcript destabilization. Furthermore, these data uncover a central role for translational repression independent of transcript destabilization in defining the downstream consequences of microRNA loss.

Rationalizing Context-Dependent Performance of Dynamic RNA Regulatory Devices

The ability of RNA to sense, regulate, and store information is an attractive attribute for a variety of functional applications including the development of regulatory control devices for synthetic biology. RNA folding and function is known to be highly context sensitive, which limits the modularity and reuse of RNA regulatory devices to control different heterologous sequences and genes. We explored the cause and effect of sequence context sensitivity for translational ON riboswitches located in the 5' UTR, by constructing and screening a library of N-terminal synonymous codon variants. By altering the N-terminal codon usage we were able to obtain RNA devices with a broad range of functional performance properties (ON, OFF, fold-change). Linear regression and calculated metrics were used to rationalize the major determining features leading to optimal riboswitch performance, and to identify multiple interactions between the explanatory metrics. Finally, partial least squared (PLS) analysis was employed in order to understand the metrics and their respective effect on performance. This PLS model was shown to provide good explanation of our library. This study provides a novel multivariant analysis framework to rationalize the codon context performance of allosteric RNA-devices. The framework will also serve as a platform for future riboswitch context engineering endeavors.

Significant differences in terms of codon usage bias between bacteriophage early and late genes: a comparative genomics analysis

Background

Viruses undergo extensive evolutionary selection for efficient replication which effects, among others, their codon distribution. In the current study, we aimed at understanding the way evolution shapes the codon distribution in early vs. late viral genes in terms of their expression during different stages in the viral replication cycle. To this end we analyzed 14 bacteriophages and 11 human viruses with available information about the expression phases of their genes.

Results

We demonstrated evidence of selection for distinct composition of synonymous codons in early and late viral genes in 50% of the analyzed bacteriophages. Among others, this phenomenon may be related to the time specific adaptation of the viral genes to the translation efficiency factors involved at different bacteriophage developmental stages. Specifically, we showed that the differences in codon composition in different temporal gene groups cannot be explained only by phylogenetic proximities between the analyzed bacteriophages, and can be partially explained by differences in the adaptation to the host tRNA pool, nucleotide bias, GC content and more.

In contrast, no difference in temporal regulation of synonymous codon usage was observed in human viruses, possibly because of a stronger selection pressure due to a larger effective population size in bacteriophages and their bacterial hosts.

Conclusions

The codon distribution in large fractions of bacteriophage genomes tend to be different in early and late genes. This phenomenon seems to be related to various aspects of the viral life cycle, and to various intracellular processes. We believe that the reported results should contribute towards better understanding of viral evolution and may promote the development of relevant procedures in synthetic virology.

Electronic supplementary material

The online version of this article (10.1186/s12864-017-4248-7) contains supplementary material, which is available to authorized users.