Trade-offs between tRNA abundance and mRNA secondary structure support smoothing of translation elongation rate

Reversing the relative time courses of the peptide bond reaction with oligopeptides of different lengths and charged amino acid distributions in the ribosome exit tunnel

The kinetics of the protein elongation cycle by the ribosome depends on intertwined factors. One of these factors is the electrostatic interaction of the nascent protein with the ribosome exit tunnel. In this computational biology theoretical study, we focus on the rate of the peptide bond formation and its dependence on the ribosome exit tunnel electrostatic potential profile. We quantitatively predict how oligopeptides of variable lengths can affect the peptide bond formation rate. We applied the Michaelis-Menten model as previously extended to incorporate the mechano-biochemical effects of forces on the rate of reaction at the catalytic site of the ribosome. For a given pair of carboxy-terminal amino acid substrate at the P- and an aminoacyl-tRNA at the A-sites, the relative time courses of the peptide bond formation reaction can be reversed depending on the oligopeptide sequence embedded in the tunnel and their variable lengths from the P-site. The reversal is predicted to occur from a shift in positions of charged amino acids upstream in the oligopeptidyl-tRNA at the P-site. The position shift must be adjusted by clever design of the oligopeptide probes using the electrostatic potential profile along the exit tunnel axial path. These predicted quantitative results bring strong evidence of the importance and relative contribution of the electrostatic interaction of the ribosome exit tunnel with the nascent peptide chain during elongation.

Transfer learning for cross-context prediction of protein expression from 5'UTR sequence

Model-guided DNA sequence design can accelerate the reprogramming of living cells. It allows us to engineer more complex biological systems by removing the need to physically assemble and test each potential design. While mechanistic models of gene expression have seen some success in supporting this goal, data-centric, deep learning-based approaches often provide more accurate predictions. This accuracy, however, comes at a cost - a lack of generalization across genetic and experimental contexts that has limited their wider use outside the context in which they were trained. Here, we address this issue by demonstrating how a simple transfer learning procedure can effectively tune a pre-trained deep learning model to predict protein translation rate from 5' untranslated region (5'UTR) sequence for diverse contexts in Escherichia coli using a small number of new measurements. This allows for important model features learnt from expensive massively parallel reporter assays to be easily transferred to new settings. By releasing our trained deep learning model and complementary calibration procedure, this study acts as a starting point for continually refined model-based sequence design that builds on previous knowledge and future experimental efforts.

Altered expression of transfer RNAs and their possible roles in brain white matter injury

Transfer RNAs (tRNAs) can regulate cell behavior and are associated with neurological disorders. Here, we aimed to investigate the expression levels of tRNAs in oligodendrocyte precursor cells (OPCs) and their possible roles in the regulation of brain white matter injury (WMI). Newborn Sprague-Dawley rats (postnatal day 5) were used to establish a model that mimicked neonatal brain WMI. RNA-array analysis was performed to examine the expression of tRNAs in OPCs. psRNAtarget software was used to predict target mRNAs of significantly altered tRNAs. Gene ontology (GO) and KEGG were used to analyze the pathways for target mRNAs. Eighty-nine tRNAs were changed after WMI (fold change absolute ≥1.5, P  < 0.01), with 31 downregulated and 58 upregulated. Among them, three significantly changed tRNAs were identified, with two being significantly increased (chr10.trna1314-ProTGG and chr2.trna2771-ProAGG) and one significantly decreased (chr10.trna11264-GlyTCC). Further, target mRNA prediction and GO/KEGG pathway analysis indicated that the target mRNAs of these tRNAs are mainly involved in G-protein coupled receptor signaling pathways and beta-alanine metabolism, which are both related to myelin formation. In summary, the expression of tRNAs in OPCs was significantly altered after brain WMI, suggesting that tRNAs may play important roles in regulating WMI. This improves the knowledge about WMI pathophysiology and may provide novel treatment targets for WMI.

Identification of an N-terminal tag (580N) that improves the biosynthesis of fluorescent proteins in Francisella tularensis and other Gram-negative bacteria

Utilization of fluorescent proteins is widespread for the study of microbial pathogenesis and host-pathogen interactions. Here, we discovered that linkage of the 36 N-terminal amino acids of FTL_0580 (a hypothetical protein of Francisella tularensis) to fluorescent proteins increases the fluorescence emission of bacteria that express these recombinant fusions. This N-terminal peptide will be referred to as 580N. Western blotting revealed that the linkage of 580N to Emerald Green Fluorescent Protein (EmGFP) in F. tularensis markedly improved detection of this protein. We therefore hypothesized that transcripts containing 580N may be translated more efficiently than those lacking the coding sequence for this leader peptide. In support, expression of emGFPFt that had been codon-optimized for F. tularensis, yielded significantly enhanced fluorescence than its non-optimized counterpart. Furthermore, fusing emGFP with coding sequence for a small N-terminal peptide (Serine-Lysine-Isoleucine-Lysine), which had previously been shown to inhibit ribosomal stalling, produced robust fluorescence when expressed in F. tularensis. These findings support the interpretation that 580N enhances the translation efficiency of fluorescent proteins in F. tularensis. Interestingly, expression of non-optimized 580N-emGFP produced greater fluorescence intensity than any other construct. Structural predictions suggested that RNA secondary structure also may be influencing translation efficiency. When expressed in Escherichia coli and Klebsiella pneumoniae bacteria, 580N-emGFP produced increased green fluorescence compared to untagged emGFP (neither allele was codon optimized for these bacteria). In conclusion, fusing the coding sequence for the 580N leader peptide to recombinant genes might serve as an economical alternative to codon optimization for enhancing protein expression in bacteria.

Tailor made: the art of therapeutic mRNA design

mRNA medicine is a new and rapidly developing field in which the delivery of genetic information in the form of mRNA is used to direct therapeutic protein production in humans. This approach, which allows for the quick and efficient identification and optimization of drug candidates for both large populations and individual patients, has the potential to revolutionize the way we prevent and treat disease. A key feature of mRNA medicines is their high degree of designability, although the design choices involved are complex. Maximizing the production of therapeutic proteins from mRNA medicines requires a thorough understanding of how nucleotide sequence, nucleotide modification and RNA structure interplay to affect translational efficiency and mRNA stability. In this Review, we describe the principles that underlie the physical stability and biological activity of mRNA and emphasize their relevance to the myriad considerations that factor into therapeutic mRNA design.

The Secondary Structure of Potato Spindle Tuber Viroid Determines Its Infectivity in Nicotiana benthamiana

The function of RNAs is determined by their structure. However, studying the relationship between RNA structure and function often requires altering RNA sequences to modify the structures, which leads to the neglect of the importance of RNA sequences themselves. In our research, we utilized potato spindle tuber viroid (PSTVd), a circular-form non-coding infectious RNA, as a model with which to investigate the role of a specific rod-like structure in RNA function. By generating linear RNA transcripts with different start sites, we established 12 PSTVd forms with different secondary structures while maintaining the same sequence. The RNA secondary structures were predicted using the mfold tool and validated through native PAGE gel electrophoresis after in vitro RNA folding. Analysis using plant infection assays revealed that the formation of a correct rod-like structure is crucial for the successful infection of PSTVd. Interestingly, the inability of PSTVd forms with non-rod-like structures to infect plants could be partially compensated by increasing the amount of linear viroid RNA transcripts, suggesting the existence of additional RNA secondary structures, such as the correct rod-like structure, alongside the dominant structure in the RNA inoculum of these forms. Our study demonstrates the critical role of RNA secondary structures in determining the function of infectious RNAs.

Bioinformatic Analysis Reveals the Role of Translation Elongation Efficiency Optimisation in the Evolution of Ralstonia Genus

Translation efficiency modulates gene expression in prokaryotes. The comparative analysis of translation elongation efficiency characteristics of Ralstonia genus bacteria genomes revealed that these characteristics diverge in accordance with the phylogeny of Ralstonia. The first branch of this genus is a group of bacteria commonly found in moist environments such as soil and water that includes the species R. mannitolilytica, R. insidiosa, and R. pickettii, which are also described as nosocomial infection pathogens. In contrast, the second branch is plant pathogenic bacteria consisting of R. solanacearum, R. pseudosolanacearum, and R. syzygii. We found that the soil Ralstonia have a significantly lower number and energy of potential secondary structures in mRNA and an increased role of codon usage bias in the optimization of highly expressed genes' translation elongation efficiency, not only compared to phytopathogenic Ralstonia but also to Cupriavidus necator, which is closely related to the Ralstonia genus. The observed alterations in translation elongation efficiency of orthologous genes are also reflected in the difference of potentially highly expressed gene' sets' content among Ralstonia branches with different lifestyles. Analysis of translation elongation efficiency characteristics can be considered a promising approach for studying complex mechanisms that determine the evolution and adaptation of bacteria in various environments.

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.

Analysis of proteomic changes in cassava cv. Kasetsart 50 caused by Sri Lankan cassava mosaic virus infection

BACKGROUND

Sri Lankan cassava mosaic virus (SLCMV) is a plant virus causing significant economic losses throughout Southeast Asia. While proteomics has the potential to identify molecular markers that could assist the breeding of virus resistant cultivars, the effects of SLCMV infection in cassava have not been previously explored in detail.

RESULTS

Liquid Chromatography-Tandem Mass Spectrometry (LC/MS-MS) was used to identify differentially expressed proteins in SLCMV infected leaves, and qPCR was used to confirm changes at mRNA levels. LC/MS-MS identified 1,813 proteins, including 479 and 408 proteins that were upregulated in SLCMV-infected and healthy cassava plants respectively, while 109 proteins were detected in both samples. Most of the identified proteins were involved in biosynthetic processes (29.8%), cellular processes (20.9%), and metabolism (18.4%). Transport proteins, stress response molecules, and proteins involved in signal transduction, plant defense responses, photosynthesis, and cellular respiration, although present, only represented a relatively small subset of the detected differences. RT-qPCR confirmed the upregulation of WRKY 77 (A0A140H8T1), WRKY 83 (A0A140H8T7), NAC 6 (A0A0M4G3M4), NAC 35 (A0A0M5JAB4), NAC 22 (A0A0M5J8Q6), NAC 54 (A0A0M4FSG8), NAC 70 (A0A0M4FEU9), MYB (A0A2C9VER9 and A0A2C9VME6), bHLH (A0A2C9UNL9 and A0A2C9WBZ1) transcription factors. Additional upregulated transcripts included receptors, such as receptor-like serine/threonine-protein kinase (RSTK) (A0A2C9UPE4), Toll/interleukin-1 receptor (TIR) (A0A2C9V5Q3), leucine rich repeat N-terminal domain (LRRNT_2) (A0A2C9VHG8), and cupin (A0A199UBY6). These molecules participate in innate immunity, plant defense mechanisms, and responses to biotic stress and to phytohormones.

CONCLUSIONS

We detected 1,813 differentially expressed proteins infected cassava plants, of which 479 were selectively upregulated. These could be classified into three main biological functional groups, with roles in gene regulation, plant defense mechanisms, and stress responses. These results will help identify key proteins affected by SLCMV infection in cassava plants.

Global and gene-specific translational regulation in Escherichia coli across different conditions

How well mRNA transcript levels represent protein abundances has been a controversial issue. Particularly across different environments, correlations between mRNA and protein exhibit remarkable variability from gene to gene. Translational regulation is likely to be one of the key factors contributing to mismatches between mRNA level and protein abundance in bacteria. Here, we quantified genome-wide transcriptome and relative translation efficiency (RTE) under 12 different conditions in Escherichia coli. By quantifying the mRNA-RTE correlation both across genes and across conditions, we uncovered a diversity of gene-specific translational regulations, cooperating with transcriptional regulations, in response to carbon (C), nitrogen (N), and phosphate (P) limitations. Intriguingly, we found that many genes regulating translation are themselves subject to translational regulation, suggesting possible feedbacks. Furthermore, a random forest model suggests that codon usage partially predicts a gene's cross-condition variability in translation efficiency; such cross-condition variability tends to be an inherent quality of a gene, independent of the specific nutrient limitations. These findings broaden the understanding of translational regulation under different environments and provide novel strategies for the control of translation in synthetic biology. In addition, our data offers a resource for future multi-omics studies.

Bioinformatic Assessment of Factors Affecting the Correlation between Protein Abundance and Elongation Efficiency in Prokaryotes

Protein abundance is crucial for the majority of genetically regulated cell functions to act properly in prokaryotic organisms. Therefore, developing bioinformatic methods for assessing the efficiency of different stages of gene expression is of great importance for predicting the actual protein abundance. One of these steps is the evaluation of translation elongation efficiency based on mRNA sequence features, such as codon usage bias and mRNA secondary structure properties. In this study, we have evaluated correlation coefficients between experimentally measured protein abundance and predicted elongation efficiency characteristics for 26 prokaryotes, including non-model organisms, belonging to diverse taxonomic groups The algorithm for assessing elongation efficiency takes into account not only codon bias, but also number and energy of secondary structures in mRNA if those demonstrate an impact on predicted elongation efficiency of the ribosomal protein genes. The results show that, for a number of organisms, secondary structures are a better predictor of protein abundance than codon usage bias. The bioinformatic analysis has revealed several factors associated with the value of the correlation coefficient. The first factor is the elongation efficiency optimization type-the organisms whose genomes are optimized for codon usage only have significantly higher correlation coefficients. The second factor is taxonomical identity-bacteria that belong to the class Bacilli tend to have higher correlation coefficients among the analyzed set. The third is growth rate, which is shown to be higher for the organisms with higher correlation coefficients between protein abundance and predicted translation elongation efficiency. The obtained results can be useful for further improvement of methods for protein abundance prediction.

The High Mutational Sensitivity of ccdA Antitoxin Is Linked to Codon Optimality

Deep mutational scanning studies suggest that synonymous mutations are typically silent and that most exposed, nonactive-site residues are tolerant to mutations. Here, we show that the ccdA antitoxin component of the Escherichia coli ccdAB toxin-antitoxin system is unusually sensitive to mutations when studied in the operonic context. A large fraction (∼80%) of single-codon mutations, including many synonymous mutations in the ccdA gene shows inactive phenotype, but they retain native-like binding affinity towards cognate toxin, CcdB. Therefore, the observed phenotypic effects are largely not due to alterations in protein structure/stability, consistent with a large region of CcdA being intrinsically disordered. E. coli codon preference and strength of ribosome-binding associated with translation of downstream ccdB gene are found to be major contributors of the observed ccdA mutant phenotypes. In select cases, proteomics studies reveal altered ratios of CcdA:CcdB protein levels in vivo, suggesting that the ccdA mutations likely alter relative translation efficiencies of the two genes in the operon. We extend these results by studying single-site synonymous mutations that lead to loss of function phenotypes in the relBE operon upon introduction of rarer codons. Thus, in their operonic context, genes are likely to be more sensitive to both synonymous and nonsynonymous point mutations than inferred previously.

The tRNA regulome in neurodevelopmental and neuropsychiatric disease

Transfer (t)RNAs are 70-90 nucleotide small RNAs highly regulated by 43 different types of epitranscriptomic modifications and requiring aminoacylation ('charging') for mRNA decoding and protein synthesis. Smaller cleavage products of mature tRNAs, or tRNA fragments, have been linked to a broad variety of noncanonical functions, including translational inhibition and modulation of the immune response. Traditionally, knowledge about tRNA regulation in brain is derived from phenotypic exploration of monogenic neurodevelopmental and neurodegenerative diseases associated with rare mutations in tRNA modification genes. More recent studies point to the previously unrecognized potential of the tRNA regulome to affect memory, synaptic plasticity, and affective states. For example, in mature cortical neurons, cytosine methylation sensitivity of the glycine tRNA family (tRNA^Gly) is coupled to glycine biosynthesis and codon-specific alterations in ribosomal translation together with robust changes in cognition and depression-related behaviors. In this Review, we will discuss the emerging knowledge of the neuronal tRNA landscape, with a focus on epitranscriptomic tRNA modifications and downstream molecular pathways affected by alterations in tRNA expression, charging levels, and cleavage while mechanistically linking these pathways to neuropsychiatric disease and provide insight into future areas of study for this field.

The Ramp Atlas: facilitating tissue and cell-specific ramp sequence analyses through an intuitive web interface

Ramp sequences occur when the average translational efficiency of codons near the 5′ end of highly expressed genes is significantly lower than the rest of the gene sequence, which counterintuitively increases translational efficiency by decreasing downstream ribosomal collisions. Here, we show that the relative codon adaptiveness within different tissues changes the existence of a ramp sequence without altering the underlying genetic code. We present the first comprehensive analysis of tissue and cell type-specific ramp sequences and report 3108 genes with ramp sequences that change between tissues and cell types, which corresponds with increased gene expression within those tissues and cells. The Ramp Atlas (https://ramps.byu.edu/) allows researchers to query precomputed ramp sequences in 18 388 genes across 62 tissues and 66 cell types and calculate tissue-specific ramp sequences from user-uploaded FASTA files through an intuitive web interface. We used The Ramp Atlas to identify seven SARS-CoV-2 genes and seven human SARS-CoV-2 entry factor genes with tissue-specific ramp sequences that may help explain viral proliferation within those tissues. We anticipate that The Ramp Atlas will facilitate personalized and creative tissue-specific ramp sequence analyses for both human and viral genes that will increase our ability to utilize this often-overlooked regulatory region.

Inferring Potential Cancer Driving Synonymous Variants

Synonymous single nucleotide variants (sSNVs) are often considered functionally silent, but a few cases of cancer-causing sSNVs have been reported. From available databases, we collected four categories of sSNVs: germline, somatic in normal tissues, somatic in cancerous tissues, and putative cancer drivers. We found that screening sSNVs for recurrence among patients, conservation of the affected genomic position, and synVep prediction (synVep is a machine learning-based sSNV effect predictor) recovers cancer driver variants (termed proposed drivers) and previously unknown putative cancer genes. Of the 2.9 million somatic sSNVs found in the COSMIC database, we identified 2111 proposed cancer driver sSNVs. Of these, 326 sSNVs could be further tagged for possible RNA splicing effects, RNA structural changes, and affected RBP motifs. This list of proposed cancer driver sSNVs provides computational guidance in prioritizing the experimental evaluation of synonymous mutations found in cancers. Furthermore, our list of novel potential cancer genes, galvanized by synonymous mutations, may highlight yet unexplored cancer mechanisms.

Characterization of tRNA expression profiles in large offspring syndrome

Background

Assisted Reproductive Technologies (ART) use can increase the risk of congenital overgrowth syndromes, such as large offspring syndrome (LOS) in ruminants. Epigenetic variations are known to influence gene expression and differentially methylated regions (DMRs) were previously determined to be associated with LOS in cattle. We observed DMRs overlapping tRNA clusters which could affect tRNA abundance and be associated with tissue specificity or overgrowth. Variations in tRNA expression have been identified in several disease pathways suggesting an important role in the regulation of biological processes. Understanding the role of tRNA expression in cattle offers an opportunity to reveal mechanisms of regulation at the translational level. We analyzed tRNA expression in the skeletal muscle and liver tissues of day 105 artificial insemination-conceived, ART-conceived with a normal body weight, and ART-conceived bovine fetuses with a body weight above the 97^th percentile compared to Control-AI.

Results

Despite the centrality of tRNAs to translation, in silico predictions have revealed dramatic differences in the number of tRNA genes between humans and cattle (597 vs 1,659). Consistent with reports in human, only a fraction of predicted tRNA genes are expressed. We detected the expression of 474 and 487 bovine tRNA genes in the muscle and liver with the remainder being unexpressed. 193 and 198 unique tRNA sequences were expressed in all treatment groups within muscle and liver respectively. In addition, an average of 193 tRNA sequences were expressed within the same treatment group in different tissues. Some tRNA isodecoders were differentially expressed between treatment groups. In the skeletal muscle and liver, we categorized 11 tRNA isoacceptors with undetected expression as well as an isodecoder that was unexpressed in the liver (Ser^GGA). Our results identified variation in the proportion of tRNA gene copies expressed between tissues and differences in the highest contributing tRNA anticodon within an amino acid family due to treatment and tissue type. Out of all amino acid families, roughly half of the most highly expressed tRNA isoacceptors correlated to their most frequent codon in the bovine genome.

Conclusion

Although the number of bovine tRNA genes is nearly triple of that of the tRNA genes in human, there is a shared occurrence of transcriptionally inactive tRNA genes in both species. We detected differential expression of tRNA genes as well as tissue- and treatment- specific tRNA transcripts with unique sequence variations that could modulate translation during protein homeostasis or cellular stress, and give rise to regulatory products targeting genes related to overgrowth in the skeletal muscle and/or tumor development in the liver of LOS individuals. While the absence of certain isodecoders may be relieved by wobble base pairing, missing tRNA species could increase the likelihood of mistranslation or mRNA degradation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08496-7.

RNA folding using quantum computers

The 3-dimensional fold of an RNA molecule is largely determined by patterns of intramolecular hydrogen bonds between bases. Predicting the base pairing network from the sequence, also referred to as RNA secondary structure prediction or RNA folding, is a nondeterministic polynomial-time (NP)-complete computational problem. The structure of the molecule is strongly predictive of its functions and biochemical properties, and therefore the ability to accurately predict the structure is a crucial tool for biochemists. Many methods have been proposed to efficiently sample possible secondary structure patterns. Classic approaches employ dynamic programming, and recent studies have explored approaches inspired by evolutionary and machine learning algorithms. This work demonstrates leveraging quantum computing hardware to predict the secondary structure of RNA. A Hamiltonian written in the form of a Binary Quadratic Model (BQM) is derived to drive the system toward maximizing the number of consecutive base pairs while jointly maximizing the average length of the stems. A Quantum Annealer (QA) is compared to a Replica Exchange Monte Carlo (REMC) algorithm programmed with the same objective function, with the QA being shown to be highly competitive at rapidly identifying low energy solutions. The method proposed in this study was compared to three algorithms from literature and, despite its simplicity, was found to be competitive on a test set containing known structures with pseudoknots.

The recent FDA approval of mRNA-based vaccines has increased public interest in synthetically designed RNA molecules. RNA molecules fold into complex secondary structures which determine their molecular properties and in part their efficacy. Determining the folded structure of an RNA molecule is a computationally challenging task with exponential scaling that is intractable to solve exactly, and therefore approximate methods are used. Quantum computing technology offers a new approach to finding approximate solutions to problems with exponential scaling. We formulate a simplistic, yet effective, model of RNA folding that can easily be mapped to quantum computers and we show that currently available quantum computing hardware is competitive with classical methods.

Specific length and structure rather than high thermodynamic stability enable regulatory mRNA stem-loops to pause translation

Translating ribosomes unwind mRNA secondary structures by three basepairs each elongation cycle. Despite the ribosome helicase, certain mRNA stem-loops stimulate programmed ribosomal frameshift by inhibiting translation elongation. Here, using mutagenesis, biochemical and single-molecule experiments, we examine whether high stability of three basepairs, which are unwound by the translating ribosome, is critical for inducing ribosome pauses. We find that encountering frameshift-inducing mRNA stem-loops from the E. coli dnaX mRNA and the gag-pol transcript of Human Immunodeficiency Virus (HIV) hinders A-site tRNA binding and slows down ribosome translocation by 15-20 folds. By contrast, unwinding of first three basepairs adjacent to the mRNA entry channel slows down the translating ribosome by only 2-3 folds. Rather than high thermodynamic stability, specific length and structure enable regulatory mRNA stem-loops to stall translation by forming inhibitory interactions with the ribosome. Our data provide the basis for rationalizing transcriptome-wide studies of translation and searching for novel regulatory mRNA stem-loops.

Ribosome exit tunnel electrostatics

The impact of ribosome exit tunnel electrostatics on the protein elongation rate or on forces acting upon the nascent polypeptide chain are currently not fully elucidated. In the past, researchers have measured the electrostatic potential inside the ribosome polypeptide exit tunnel at a limited number of spatial points, at least in rabbit reticulocytes. Here we present a basic electrostatic model of the exit tunnel of the ribosome, providing a quantitative physical description of the tunnel interaction with the nascent proteins at all centro-axial points inside the tunnel. We show that a strong electrostatic screening is due to water molecules (not mobile ions) attracted to the ribosomal nucleic acid phosphate moieties buried in the immediate vicinity of the tunnel wall. We also show how the tunnel wall components and local ribosomal protein protrusions impact on the electrostatic potential profile and impede charged amino acid residues from progressing through the tunnel, affecting the elongation rate in a range of -40% to +85% when compared to the average elongation rate. The time spent by the ribosome to decode the genetic encrypted message is constrained accordingly. We quantitatively derive, at single-residue resolution, the axial forces acting on the nascent peptide from its particular sequence embedded in the tunnel. The model sheds light on how the experimental data point measurements of the potential are linked to the local structural chemistry of the inner wall, shape, and size of the tunnel. The model consistently connects experimental observations coming from different fields in molecular biology, x-ray crystallography, physical chemistry, biomechanics, and synthetic and multiomics biology. Our model should be a valuable tool to gain insight into protein synthesis dynamics, translational control, and the role of the ribosome's mechanochemistry in the cotranslational protein folding.

Controlling selectivity of modular microbial biosynthesis of butyryl-CoA-derived designer esters

Short-chain esters have broad utility as flavors, fragrances, solvents, and biofuels. Controlling selectivity of ester microbial biosynthesis has been an outstanding metabolic engineering problem. In this study, we enabled the de novo fermentative microbial biosynthesis of butyryl-CoA-derived designer esters (e.g., butyl acetate, ethyl butyrate, butyl butyrate) in Escherichia coli with controllable selectivity. Using the modular design principles, we generated the butyryl-CoA-derived ester pathways as exchangeable production modules compatible with an engineered chassis cell for anaerobic production of designer esters. We designed these modules derived from an acyl-CoA submodule (e.g., acetyl-CoA, butyryl-CoA), an alcohol submodule (e.g., ethanol, butanol), a cofactor regeneration submodule (e.g., NADH), and an alcohol acetyltransferase (AAT) submodule (e.g., ATF1, SAAT) for rapid module construction and optimization by manipulating replication (e.g., plasmid copy number), transcription (e.g., promoters), translation (e.g., codon optimization), pathway enzymes, and pathway induction conditions. To further enhance production of designer esters with high selectivity, we systematically screened various strategies of protein solubilization using protein fusion tags and chaperones to improve the soluble expression of multiple pathway enzymes. Finally, our engineered ester-producing strains could achieve 19-fold increase in butyl acetate production (0.64 g/L, 96% selectivity), 6-fold increase in ethyl butyrate production (0.41 g/L, 86% selectivity), and 13-fold increase in butyl butyrate production (0.45 g/L, 54% selectivity) as compared to the initial strains. Overall, this study presented a generalizable framework to engineer modular microbial platforms for anaerobic production of butyryl-CoA-derived designer esters from renewable feedstocks.

Codon optimality in cancer

A key characteristic of cancer cells is their increased proliferative capacity, which requires elevated levels of protein synthesis. The process of protein synthesis involves the translation of codons within the mRNA coding sequence into a string of amino acids to form a polypeptide chain. As most amino acids are encoded by multiple codons, the nucleotide sequence of a coding region can vary dramatically without altering the polypeptide sequence of the encoded protein. Although mutations that do not alter the final amino acid sequence are often thought of as silent/synonymous, these can still have dramatic effects on protein output. Because each codon has a distinct translation elongation rate and can differentially impact mRNA stability, each codon has a different degree of 'optimality' for protein synthesis. Recent data demonstrates that the codon preference of a transcriptome matches the abundance of tRNAs within the cell and that this supply and demand between tRNAs and mRNAs varies between different cell types. The largest observed distinction is between mRNAs encoding proteins associated with proliferation or differentiation. Nevertheless, precisely how codon optimality and tRNA expression levels regulate cell fate decisions and their role in malignancy is not fully understood. This review describes the current mechanistic understanding on codon optimality, its role in malignancy and discusses the potential to target codon optimality therapeutically in the context of cancer.

A dual-reporter system for investigating and optimizing protein translation and folding in E. coli

Strategies for investigating and optimizing the expression and folding of proteins for biotechnological and pharmaceutical purposes are in high demand. Here, we describe a dual-reporter biosensor system that simultaneously assesses in vivo protein translation and protein folding, thereby enabling rapid screening of mutant libraries. We have validated the dual-reporter system on five different proteins and find an excellent correlation between reporter signals and the levels of protein expression and solubility of the proteins. We further demonstrate the applicability of the dual-reporter system as a screening assay for deep mutational scanning experiments. The system enables high throughput selection of protein variants with high expression levels and altered protein stability. Next generation sequencing analysis of the resulting libraries of protein variants show a good correlation between computationally predicted and experimentally determined protein stabilities. We furthermore show that the mutational experimental data obtained using this system may be useful for protein structure calculations.

Neuronal Nsun2 deficiency produces tRNA epitranscriptomic alterations and proteomic shifts impacting synaptic signaling and behavior

Epitranscriptomic mechanisms linking tRNA function and the brain proteome to cognition and complex behaviors are not well described. Here, we report bi-directional changes in depression-related behaviors after genetic disruption of neuronal tRNA cytosine methylation, including conditional ablation and transgene-derived overexpression of Nsun2 in the mouse prefrontal cortex (PFC). Neuronal Nsun2-deficiency was associated with a decrease in tRNA m5C levels, resulting in deficits in expression of 70% of tRNAGly isodecoders. Altogether, 1488/5820 proteins changed upon neuronal Nsun2-deficiency, in conjunction with glycine codon-specific defects in translational efficiencies. Loss of Gly-rich proteins critical for glutamatergic neurotransmission was associated with impaired synaptic signaling at PFC pyramidal neurons and defective contextual fear memory. Changes in the neuronal translatome were also associated with a 146% increase in glycine biosynthesis. These findings highlight the methylation sensitivity of glycinergic tRNAs in the adult PFC. Furthermore, they link synaptic plasticity and complex behaviors to epitranscriptomic modifications of cognate tRNAs and the proteomic homeostasis associated with specific amino acids.

Diversity and prevalence of ANTAR RNAs across actinobacteria

Background

Computational approaches are often used to predict regulatory RNAs in bacteria, but their success is limited to RNAs that are highly conserved across phyla, in sequence and structure. The ANTAR regulatory system consists of a family of RNAs (the ANTAR-target RNAs) that selectively recruit ANTAR proteins. This protein-RNA complex together regulates genes at the level of translation or transcriptional elongation. Despite the widespread distribution of ANTAR proteins in bacteria, their target RNAs haven’t been identified in certain bacterial phyla such as actinobacteria.

Results

Here, by using a computational search model that is tuned to actinobacterial genomes, we comprehensively identify ANTAR-target RNAs in actinobacteria. These RNA motifs lie in select transcripts, often overlapping with the ribosome binding site or start codon, to regulate translation. Transcripts harboring ANTAR-target RNAs majorly encode proteins involved in the transport and metabolism of cellular metabolites like sugars, amino acids and ions; or encode transcription factors that in turn regulate diverse genes.

Conclusion

In this report, we substantially diversify and expand the family of ANTAR RNAs across bacteria. These findings now provide a starting point to investigate the actinobacterial processes that are regulated by ANTAR.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02234-x.

CDH1 gene rs1801552 C/T polymorphism increases susceptibility to esophageal squamous cell carcinoma but not to gastric cardiac adenocarcinoma

PURPOSE

The present study aimed to investigate whether the single nucleotide polymorphism (SNP) rs1801552 C/T in CDH1 gene is correlated with the risk of esophageal squamous cell carcinoma (ESCC) and gastric cardiac adenocarcinoma (GCA), as a preliminary study.

METHODS

The rs1801552 C/T polymorphism was genotyped by the method of polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) in 1316 cancer patients (810 ESCC and 506 GCA) and 1966 controls in north China. We performed two case-control studies, each of which included a population-based set and a hospital-based set.

RESULTS

The data showed that the rs1801552 C/T polymorphism was associated with the risk of ESCC. Allelotype and genotype distributions of the rs1801552 C/T polymorphism in ESCC patients of high-incidence region and hospital were significantly different from that in their respective controls (P < 0.05). Compared with C/C genotype, T/T genotype increased the risk of ESCC in high-incidence region and hospital (age, sex, smoking status and family history of UGIC adjusted odds ratio (OR) = 1.79 and 2.10, 95% confidence interval (CI) = 1.23 - 2.60 and 1.10 - 4.04, respectively). Allelotype and genotype distributions of the rs1801552 C/T polymorphism in GCA patients were not significantly different from that in their controls, respectively (P > 0.05).

CONCLUSIONS

The findings in the present pilot study suggest that the rs1801552 C/T polymorphism was associated with the risk of ESCC, but was not associated with the risk of GCA in high-incidence region and hospital.

Wobble tRNA modification and hydrophilic amino acid patterns dictate protein fate

Regulation of mRNA translation elongation impacts nascent protein synthesis and integrity and plays a critical role in disease establishment. Here, we investigate features linking regulation of codon-dependent translation elongation to protein expression and homeostasis. Using knockdown models of enzymes that catalyze the mcm5s2 wobble uridine tRNA modification (U34-enzymes), we show that gene codon content is necessary but not sufficient to predict protein fate. While translation defects upon perturbation of U34-enzymes are strictly dependent on codon content, the consequences on protein output are determined by other features. Specific hydrophilic motifs cause protein aggregation and degradation upon codon-dependent translation elongation defects. Accordingly, the combination of codon content and the presence of hydrophilic motifs define the proteome whose maintenance relies on U34-tRNA modification. Together, these results uncover the mechanism linking wobble tRNA modification to mRNA translation and aggregation to maintain proteome homeostasis.

Full-length ribosome density prediction by a multi-input and multi-output model

Translation elongation is regulated by a series of complicated mechanisms in both prokaryotes and eukaryotes. Although recent advance in ribosome profiling techniques has enabled one to capture the genome-wide ribosome footprints along transcripts at codon resolution, the regulatory codes of elongation dynamics are still not fully understood. Most of the existing computational approaches for modeling translation elongation from ribosome profiling data mainly focus on local contextual patterns, while ignoring the continuity of the elongation process and relations between ribosome densities of remote codons. Modeling the translation elongation process in full-length coding sequence (CDS) level has not been studied to the best of our knowledge. In this paper, we developed a deep learning based approach with a multi-input and multi-output framework, named RiboMIMO, for modeling the ribosome density distributions of full-length mRNA CDS regions. Through considering the underlying correlations in translation efficiency among neighboring and remote codons and extracting hidden features from the input full-length coding sequence, RiboMIMO can greatly outperform the state-of-the-art baseline approaches and accurately predict the ribosome density distributions along the whole mRNA CDS regions. In addition, RiboMIMO explores the contributions of individual input codons to the predictions of output ribosome densities, which thus can help reveal important biological factors influencing the translation elongation process. The analyses, based on our interpretable metric named codon impact score, not only identified several patterns consistent with the previously-published literatures, but also for the first time (to the best of our knowledge) revealed that the codons located at a long distance from the ribosomal A site may also have an association on the translation elongation rate. This finding of long-range impact on translation elongation velocity may shed new light on the regulatory mechanisms of protein synthesis. Overall, these results indicated that RiboMIMO can provide a useful tool for studying the regulation of translation elongation in the range of full-length CDS.

Translation elongation is a process in which amino acids are linked into proteins by ribosomes in cells. Translation elongation rates along the mRNAs are not constant, and are regulated by a series of mechanisms, such as codon rarity and mRNA stability. In this study, we modeled the translation elongation process at a full-length coding sequence level and developed a deep learning based approach to predict the translation elongation rates from mRNA sequences, through extracting the regulatory codes of elongation rates from the contextual sequences. The analyses, based on our interpretable metric named codon impact score, for the first time (to the best of our knowledge), revealed that in addition to the neighboring codons of the ribosomal A sites, the remote codons may also have an important impact on the translation elongation rates. This new finding may stimulate additional experiments and shed light on the regulatory mechanisms of protein synthesis.

Massively parallel gene expression variation measurement of a synonymous codon library

BACKGROUND

Cell-to-cell variation in gene expression strongly affects population behavior and is key to multiple biological processes. While codon usage is known to affect ensemble gene expression, how codon usage influences variation in gene expression between single cells is not well understood.

RESULTS

Here, we used a Sort-seq based massively parallel strategy to quantify gene expression variation from a green fluorescent protein (GFP) library containing synonymous codons in Escherichia coli. We found that sequences containing codons with higher tRNA Adaptation Index (TAI) scores, and higher codon adaptation index (CAI) scores, have higher GFP variance. This trend is not observed for codons with high Normalized Translation Efficiency Index (nTE) scores nor from the free energy of folding of the mRNA secondary structure. GFP noise, or squared coefficient of variance (CV2), scales with mean protein abundance for low-abundant proteins but does not change at high mean protein abundance.

CONCLUSIONS

Our results suggest that the main source of noise for high-abundance proteins is likely not originating at translation elongation. Additionally, the drastic change in mean protein abundance with small changes in protein noise seen from our library implies that codon optimization can be performed without concerning gene expression noise for biotechnology applications.

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.

Combating Antimicrobial Resistance With New-To-Nature Lanthipeptides Created by Genetic Code Expansion

Due to the rapid emergence of multi-resistant bacterial strains in recent decades, the commercially available effective antibiotics are becoming increasingly limited. On the other hand, widespread antimicrobial peptides (AMPs) such as the lantibiotic nisin has been used worldwide for more than 40 years without the appearance of significant bacterial resistance. Lantibiotics are ribosomally synthesized antimicrobials generated by posttranslational modifications. Their biotechnological production is of particular interest to redesign natural scaffolds improving their pharmaceutical properties, which has great potential for therapeutic use in human medicine and other areas. However, conventional protein engineering methods are limited to 20 canonical amino acids prescribed by the genetic code. Therefore, the expansion of the genetic code as the most advanced approach in Synthetic Biology allows the addition of new amino acid building blocks (non-canonical amino acids, ncAAs) during protein translation. We now have solid proof-of-principle evidence that bioexpression with these novel building blocks enabled lantibiotics with chemical properties transcending those produced by natural evolution. The unique scaffolds with novel structural and functional properties are the result of this bioengineering. Here we will critically examine and evaluate the use of the expanded genetic code and its alternatives in lantibiotics research over the last 7 years. We anticipate that Synthetic Biology, using engineered lantibiotics and even more complex scaffolds will be a promising tool to address an urgent problem of antibiotic resistance, especially in a class of multi-drug resistant microbes known as superbugs.

A possible universal role for mRNA secondary structure in bacterial translation revealed using a synthetic operon

In bacteria, translation re-initiation is crucial for synthesizing proteins encoded by genes that are organized into operons. The mechanisms regulating translation re-initiation remain, however, poorly understood. We now describe the ribosome termination structure (RTS), a conserved and stable mRNA secondary structure localized immediately downstream of stop codons, and provide experimental evidence for its role in governing re-initiation efficiency in a synthetic Escherichia coli operon. We further report that RTSs are abundant, being associated with 18%–65% of genes in 128 analyzed bacterial genomes representing all phyla, and are selectively depleted when translation re-initiation is advantageous yet selectively enriched so as to insulate translation when re-initiation is deleterious. Our results support a potentially universal role for the RTS in controlling translation termination-insulation and re-initiation across bacteria.

The mechanisms for regulating translation re-initiation in bacteria remain poorly understood. Here, the authors screened a library of synthetic operons and identified a ribosome termination structure that modulates re-initiation efficiency and which is conserved across bacteria.

Comparative study of concatemer efficiency as an isotope-labelled internal standard for allergen quantification

Mass spectrometry-based methods coupled with stable isotope dilution have become effective and widely used methods for the detection and quantification of food allergens. Current methods target signature peptides resulting from proteolytic digestion of proteins of the allergenic ingredient. The choice of appropriate stable isotope-labelled internal standard is crucial, given the diversity of encountered food matrices which can affect sample preparation and analysis. We propose the use of concatemer, an artificial and stable isotope-labelled protein composed of several concatenated signature peptides as internal standard. With a comparative analysis of three matrices contaminated with four allergens (egg, milk, peanut, and hazelnut), the concatemer approach was found to offer advantages associated with the use of labelled proteins, ideal but unaffordable, and circumvent certain limitations of traditionally used synthetic peptides as internal standards. Although used in the proteomic field for more than a decade, concatemer strategy has not yet been applied for food analysis.

A report on DNA sequence determinants in gene expression

The biased usage of nucleotides in coding sequence and its correlation with gene expression has been observed in several studies. A complex set of interactions between genes and other components of the expression system determine the amount of proteins produced from coding sequences. It is known that the elongation rate of polypeptide chain is affected by both codon usage bias and specific amino acid compositional constraints. Therefore, it is of interest to review local DNA-sequence elements and other positional as well as combinatorial constraints that play significant role in gene expression.

Robust landscapes of ribosome dwell times and aminoacyl-tRNAs in response to nutrient stress in liver

Translation depends on messenger RNA (mRNA)-specific initiation, elongation, and termination rates. While translation elongation is well studied in bacteria and yeast, less is known in higher eukaryotes. Here we combined ribosome and transfer RNA (tRNA) profiling to investigate the relations between translation elongation rates, (aminoacyl-) tRNA levels, and codon usage in mammals. We modeled codon-specific ribosome dwell times from ribosome profiling, considering codon pair interactions between ribosome sites. In mouse liver, the model revealed site- and codon-specific dwell times that differed from those in yeast, as well as pairs of adjacent codons in the P and A site that markedly slow down or speed up elongation. While translation efficiencies vary across diurnal time and feeding regimen, codon dwell times were highly stable and conserved in human. Measured tRNA levels correlated with codon usage and several tRNAs showed reduced aminoacylation, which was conserved in fasted mice. Finally, we uncovered that the longest codon dwell times could be explained by aminoacylation levels or high codon usage relative to tRNA abundance.

Widespread non-modular overlapping codes in the coding regions

Messenger RNAs (mRNAs) consist of a coding region (open reading frame (ORF)) and two untranslated regions (UTRs), 5'UTR and 3'UTR. Ribosomes travel along the coding region, translating nucleotide triplets (called codons) to a chain of amino acids. The coding region was long believed to mainly encode the amino acid content of proteins, whereas regulatory signals reside in the UTRs and in other genomic regions. However, in recent years we have learned that the ORF is expansively populated with various regulatory signals, or codes, which are related to all gene expression steps and additional intracellular aspects. In this paper, we review the current knowledge related to overlapping codes inside the coding regions, such as the influence of synonymous codon usage on translation speed (and, in turn, the effect of translation speed on protein folding), ribosomal frameshifting, mRNA stability, methylation, splicing, transcription and more. All these codes come together and overlap in the ORF sequence, ensuring production of the right protein at the right time.

Physicochemical Foundations of Life that Direct Evolution: Chance and Natural Selection are not Evolutionary Driving Forces

The current framework of evolutionary theory postulates that evolution relies on random mutations generating a diversity of phenotypes on which natural selection acts. This framework was established using a top-down approach as it originated from Darwinism, which is based on observations made of complex multicellular organisms and, then, modified to fit a DNA-centric view. In this article, it is argued that based on a bottom-up approach starting from the physicochemical properties of nucleic and amino acid polymers, we should reject the facts that (i) natural selection plays a dominant role in evolution and (ii) the probability of mutations is independent of the generated phenotype. It is shown that the adaptation of a phenotype to an environment does not correspond to organism fitness, but rather corresponds to maintaining the genome stability and integrity. In a stable environment, the phenotype maintains the stability of its originating genome and both (genome and phenotype) are reproduced identically. In an unstable environment (i.e., corresponding to variations in physicochemical parameters above a physiological range), the phenotype no longer maintains the stability of its originating genome, but instead influences its variations. Indeed, environment- and cellular-dependent physicochemical parameters define the probability of mutations in terms of frequency, nature, and location in a genome. Evolution is non-deterministic because it relies on probabilistic physicochemical rules, and evolution is driven by a bidirectional interplay between genome and phenotype in which the phenotype ensures the stability of its originating genome in a cellular and environmental physicochemical parameter-depending manner.

Overlapping spectrums: The clinicogenetic commonalities between Charcot-Marie-Tooth and other neurodegenerative diseases

Charcot-Marie-Tooth (CMT) disease is a progressive and heterogeneous inherited peripheral neuropathy. A myriad of genetic factors have been identified that contribute to the degeneration of motor and sensory axons in a length-dependent manner. Emerging biological themes underlying disease include defects in axonal trafficking, dysfunction in RNA metabolism and protein homeostasis, as well deficits in the cellular stress response. Moreover, genetic contributions to CMT can have overlap with other neuropathies, motor neuron diseases (MNDs) and neurodegenerative disorders. Recent progress in understanding the molecular biology of CMT and overlapping syndromes aids in the search for necessary therapeutic targets.

Construction of High-Quality Rice Ribosome Footprint Library

High-throughput sequencing of ribosome footprints precisely maps and quantifies in vivo mRNA translation. The ribosome footprint sequencing has undergone continuing development since its original report. Here we provide a detailed protocol for construction of high-quality ribosome footprint library of rice. Rice total polysomes are isolated with a modified low ionic polysome extraction buffer. After nuclease digestion, rice ribosome footprints are extracted using SDS method followed by column purification. High-quality rice ribosome footprint library with peak reads of approximately 28-nucleotide (nt) length and strong 3-nt periodicity is constructed via key steps including rRNA depletion, end repair, 3' adapter ligation, reverse transcription, circularization, PCR enrichment and several rounds of purification. Biological significance of rice ribosome footprint library is further revealed by the comparison of transcriptomic and translatomic responses to salt stress and the utilization for novel open reading frame (ORF) identification. This improved protocol for rice ribosome footprint library construction will facilitate the global comprehension and quantitative measurement of dynamic translation in rice.

On the role of mRNA secondary structure in bacterial translation

Messenger RNA (mRNA) is no longer considered as a mere informational molecule whose sole function is to convey the genetic information specified by DNA to the ribosome. Beyond this primary function, mRNA also contains additional instructions that influence the way and the extent to which this message is translated by the ribosome into protein(s). Indeed, owing to its intrinsic propensity to quickly and dynamically fold and form higher order structures, mRNA exhibits a second layer of structural information specified by the sequence itself. Besides influencing transcription and mRNA stability, this additional information also affects translation, and more precisely the frequency of translation initiation, the choice of open reading frame by recoding, the elongation speed, and the folding of the nascent protein. Many studies in bacteria have shown that mRNA secondary structure participates to the rapid adaptation of these versatile organisms to changing environmental conditions by efficiently tuning translation in response to diverse signals, such as the presence of ligands, regulatory proteins, or small RNAs. This article is categorized under: Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems Translation > Translation Regulation.

Expanding the DOPA Universe with Genetically Encoded, Mussel-Inspired Bioadhesives for Material Sciences and Medicine

Catechols are a biologically relevant group of aromatic diols that have attracted much attention as mediators of adhesion of "bio-glue" proteins in mussels of the genus Mytilus. These organisms use catechols in the form of the noncanonical amino acid l-3,4-dihydroxyphenylalanine (DOPA) as a building block for adhesion proteins. The DOPA is generated post-translationally from tyrosine. Herein, we review the properties, natural occurrence, and reactivity of catechols in the design of bioinspired materials. We also provide a basic description of the mussel's attachment apparatus, the interplay between its different molecules that play a crucial role in adhesion, and the role of post-translational modifications (PTMs) of these proteins. Our focus is on the microbial production of mussel foot proteins with the aid of orthogonal translation systems (OTSs) and the use of genetic code engineering to solve some fundamental problems in the bioproduction of these bioadhesives and to expand their chemical space. The major limitation of bacterial expression systems is their intrinsic inability to introduce PTMs. OTSs have the potential to overcome these challenges by replacing canonical amino acids with noncanonical ones. In this way, PTM steps are circumvented while the genetically programmed precision of protein sequences is preserved. In addition, OTSs should enable spatiotemporal control over the complex adhesion process, because the catechol function can be masked by suitable chemical protection. Such caged residues can then be noninvasively unmasked by, for example, UV irradiation or thermal treatment. All of these features make OTSs based on genetic code engineering in reprogrammed microbial strains new and promising tools in bioinspired materials science.

Deciphering the rules of mRNA structure differentiation in Saccharomyces cerevisiae in vivo and in vitro with deep neural networks

The structure of mRNA in vivo is unwound to some extent in response to multiple factors involved in the translation process, resulting in significant differences from the structure of the same mRNA in vitro. In this study, we have proposed a novel application of deep neural networks, named DeepDRU, to predict the degree of mRNA structure unwinding in vivo by fitting five quantifiable features that may affect mRNA folding: ribosome density (RD), minimum folding free energy (MFE), GC content, translation initiation ribosome density (INI) and mRNA structure position (POS). mRNA structures with adjustment of the simulated structural features were designed and then fed into the trained DeepDRU model. We found unique effect regions of these five features on mRNA structure in vivo. Strikingly, INI is the most critical factor affecting the structure of mRNA in vivo, and structural sequence features, including MFE and GC content, have relatively smaller effects. DeepDRU provides a new paradigm for predicting the unwinding capability of mRNA structure in vivo. This improved knowledge about the mechanisms of factors influencing the structural capability of mRNA to unwind will facilitate the design and functional analysis of mRNA structure in vivo.

Absolute quantification of translational regulation and burden using combined sequencing approaches

Translation of mRNAs into proteins is a key cellular process. Ribosome binding sites and stop codons provide signals to initiate and terminate translation, while stable secondary mRNA structures can induce translational recoding events. Fluorescent proteins are commonly used to characterize such elements but require the modification of a part's natural context and allow only a few parameters to be monitored concurrently. Here, we combine Ribo-seq with quantitative RNA-seq to measure at nucleotide resolution and in absolute units the performance of elements controlling transcriptional and translational processes during protein synthesis. We simultaneously measure 779 translation initiation rates and 750 translation termination efficiencies across the Escherichia coli transcriptome, in addition to translational frameshifting induced at a stable RNA pseudoknot structure. By analyzing the transcriptional and translational response, we discover that sequestered ribosomes at the pseudoknot contribute to a σ³²-mediated stress response, codon-specific pausing, and a drop in translation initiation rates across the cell. Our work demonstrates the power of integrating global approaches toward a comprehensive and quantitative understanding of gene regulation and burden in living cells.

Improved protein production and codon optimization analyses in Escherichia coli by bicistronic design

Different codon optimization algorithms are available that aim at improving protein production by optimizing translation elongation. In these algorithms, it is generally not considered how the altered protein coding sequence will affect the secondary structure of the corresponding RNA transcript, particularly not the effect on the 5'-UTR structure and related ribosome binding site availability. This is a serious drawback, because the influence of codon usage on mRNA secondary structures, especially near the start of a gene, may strongly influence translation initiation. In this study, we aim to reduce the effect of codon usage on translation initiation by applying a bicistronic design (BCD) element. Protein production of several codon-optimized gene variants is tested in parallel for a BCD and a standard monocistronic design (MCD). We demonstrate that these distinct architectures can drastically change the relative performance of different codon optimization algorithms. We conclude that a BCD is indispensable in future studies that aim to reveal the impact of codon optimization and codon usage correlations. Furthermore, irrespective of the algorithm used, using a BCD does improve protein production compared with an MCD. The overall highest expression from BCDs for both GFP and RFP is at least twofold higher than the highest levels found for the MCDs, while for codon variants having very low expression from the MCD, even 10-fold to 100-fold increases in expression were achieved by the BCD. This shows the great potential of the BCD element for recombinant protein production.

Deep scanning lysine metabolism in Escherichia coli

Our limited ability to predict genotype-phenotype relationships has called for strategies that allow testing of thousands of hypotheses in parallel. Deep scanning mutagenesis has been successfully implemented to map genotype-phenotype relationships at a single-protein scale, allowing scientists to elucidate properties that are difficult to predict. However, most phenotypes are dictated by several proteins that are interconnected through complex and robust regulatory and metabolic networks. These sophisticated networks hinder our understanding of the phenotype of interest and limit our capabilities to rewire cellular functions. Here, we leveraged CRISPR-EnAbled Trackable genome Engineering to attempt a parallel and high-resolution interrogation of complex networks, deep scanning multiple proteins associated with lysine metabolism in Escherichia coli We designed over 16,000 mutations to perturb this pathway and mapped their contribution toward resistance to an amino acid analog. By doing so, we identified different routes that can alter pathway function and flux, uncovering mechanisms that would be difficult to rationally design. This approach sets a framework for forward investigation of complex multigenic phenotypes.

Cells alter their tRNA abundance to selectively regulate protein synthesis during stress conditions

Decoding the information in mRNA during protein synthesis relies on tRNA adaptors, the abundance of which can affect the decoding rate and translation efficiency. To determine whether cells alter tRNA abundance to selectively regulate protein expression, we quantified changes in the abundance of individual tRNAs at different time points in response to diverse stress conditions in Saccharomyces cerevisiae. We found that the tRNA pool was dynamic and rearranged in a manner that facilitated selective translation of stress-related transcripts. Through genomic analysis of multiple data sets, stochastic simulations, and experiments with designed sequences of proteins with identical amino acids but altered codon usage, we showed that changes in tRNA abundance affected protein expression independently of factors such as mRNA abundance. We suggest that cells alter their tRNA abundance to selectively affect the translation rates of specific transcripts to increase the amounts of required proteins under diverse stress conditions.

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.

Following Ribosome Footprints to Understand Translation at a Genome Wide Level

Protein translation is a key step in gene expression. The development of Ribosome Profiling has allowed the global analysis of this process at sub-codon resolution. In the last years the method has been applied to several models ranging from bacteria to mammalian cells yielding a surprising amount of insight on the mechanism and the regulation of translation. In this review we describe the key aspects of the experimental protocol and comment on the main conclusions raised in different models.