Coding-sequence determinants of gene expression in Escherichia coli

Modeling coding sequence design for virus-based expression in tobacco

Transient expression in Tobacco is a popular way to produce recombinant proteins in plants. The design of various expression vectors, delivered into the plant by Agrobacterium, has enabled high production levels of some proteins. To further enhance expression, researchers often adapt the coding sequence of heterologous genes to the host, but this strategy has produced mixed results in Tobacco. To study the effects of different sequence features on protein yield, we compile a dataset of the yields and coding sequences of previously published expression studies of more than 200 coding sequences. We evaluate various established gene expression models on a subset of the expression studies. We find that use of tobacco codons is only moderately predictive of protein yield as informative sequence features likely extend over multiple codons. Additionally, we show that codon usage of organisms that use tobacco as a host for expression of their proteins in a similar way as the synthetic system, like viruses and agrobacteria, can be used to predict heterologous expression. Other predictive features are related to tRNA supply and demand, the inclusion of a translational ramp of codons with lower adaptation to the tRNA pool at the beginning of the coding region, and the amino acid composition of the recombinant protein. A model based on all the features achieved a correlation of 0.57 with protein yield. We believe that our study provides a practical guideline for coding sequence design for efficient expression in tobacco.

Research progress of mosquito-borne virus mRNA vaccines

In recent years, mRNA vaccines have emerged as a leading technology for preventing infectious diseases due to their rapid development and high immunogenicity. These vaccines encode viral antigens, which are translated into antigenic proteins within host cells, inducing both humoral and cellular immune responses. This review systematically examines the progress in mRNA vaccine research for major mosquito-borne viruses, including dengue virus, Zika virus, Japanese encephalitis virus, Chikungunya virus, yellow fever virus, Rift Valley fever virus, and Venezuelan equine encephalitis virus. Enhancements in mRNA vaccine design, such as improvements to the 5' cap structure, 5'UTR, open reading frame, 3'UTR, and polyadenylation tail, have significantly increased mRNA stability and translation efficiency. Additionally, the use of lipid nanoparticles and polymer nanoparticles has greatly improved the delivery efficiency of mRNA vaccines. Currently, mRNA vaccines against mosquito-borne viruses are under development and clinical trials, showing promising protective effects. Future research should continue to optimize vaccine design and delivery systems to achieve broad-spectrum and long-lasting protection against various mosquito-borne virus infections.

Multi-omics investigation of high-transglutaminase production mechanisms in Streptomyces mobaraensis and co-culture-enhanced fermentation strategies

Transglutaminase (TGase) has been widely applied in the food industry. However, achieving high-yield TGase production remains a challenge, limiting its broader industrial application. In this study, a high-yield strain with stable genetic traits was obtained through UV-ARTP combined mutagenesis, achieving a maximum TGase activity of 13.77 U/mL, representing a 92.43% increase. Using this strain as a forward mutation gene pool, comparative genomic research identified 95 mutated genes, which were mostly due to base substitutions that led to changes in codon usage preference. Transcriptomic analysis revealed significant expression changes in 470 genes, with 232 upregulated and 238 downregulated genes. By investigating potential key regulatory factors, comprehensive analysis indicated that changes in codon usage preference, amino acid metabolism, carbon metabolism, protein export processes, TGase activation, and spore production pathways collectively contributed to the enhancement of TGase activity. Subsequently, the in vitro activation efficiency of TGase was further improved using co-cultivation techniques with neutral proteases secreted by Bacillus amyloliquefaciens CICC10888, and a TGase activity of 16.91 U/mL was achieved, accounting for a 22.71% increase. This study provides a comprehensive understanding of the mechanisms underlying high-yield TGase production and valuable insights and data references for future research.

Genes and Pathways Comprising the Human and Mouse ORFeomes Display Distinct Codon Bias Signatures that Can Regulate Protein Levels

Arginine, glutamic acid and selenocysteine based codon bias has been shown to regulate the translation of specific mRNAs for proteins that participate in stress responses, cell cycle and transcriptional regulation. Defining codon-bias in gene networks has the potential to identify other pathways under translational control. Here we have used computational methods to analyze the ORFeome of all unique human (19,711) and mouse (22,138) open-reading frames (ORFs) to characterize codon-usage and codon-bias in genes and biological processes. We show that ORFeome-wide clustering of gene-specific codon frequency data can be used to identify ontology-enriched biological processes and gene networks, with developmental and immunological programs well represented for both humans and mice. We developed codon over-use ontology mapping and hierarchical clustering to identify multi-codon bias signatures in human and mouse genes linked to signaling, development, mitochondria and metabolism, among others. The most distinct multi-codon bias signatures were identified in human genes linked to skin development and RNA metabolism, and in mouse genes linked to olfactory transduction and ribosome, highlighting species-specific pathways potentially regulated by translation. Extreme codon bias was identified in genes that included transcription factors and histone variants. We show that re-engineering extreme usage of C- or U-ending codons for aspartic acid, asparagine, histidine and tyrosine in the transcription factors CEBPB and MIER1, respectively, significantly regulates protein levels. Our study highlights that multi-codon bias signatures can be linked to specific biological pathways and that extreme codon bias with regulatory potential exists in transcription factors for immune response and development.

D-stem mutation in an essential tRNA increases translation speed at the cost of fidelity

The efficiency with which aminoacyl-tRNA and GTP-bound translation elongation factor EF-Tu recognizes the A-site codon of the ribosome is dependent on codons and tRNA species present in the polypeptide (P) and exit (E) codon sites. To understand how codon context affects the efficiency of codon recognition by tRNA-bound EF-Tu, a genetic system was developed to select for fast translation through slow-translating codon combinations. Selection for fast translation through the slow-translated UCA-UAC pair, flanked by histidine codons, resulted in the isolation of an A25G base substitution mutant in the D-stem of an essential tRNA LeuZ, which recognizes the UUA and UUG leucine codons. The LeuZ(A25G) substitution allowed for faster translation through all codon pairs tested that included the UCA codon. Insertion of leucine at the UCA serine codon was enhanced in the presence of LeuZ(A25G) tRNA. This work, taken in context with the Hirsh UGA nonsense suppressor G24A mutation in TrpT tRNA, provides genetic evidence that the post-GTP hydrolysis proofreading step by elongation factor Tu may be controlled by structural interactions in the hinge region of tRNA species. Our results support a model in which the tRNA bending component of the accommodation step in mRNA translation allows EF Tu time to enhance its ability to differentiate tRNA interactions between cognate and near-cognate mRNA codons.

The metabolic burden associated with plasmid acquisition: An assessment of the unrecognized benefits to host cells

Bacterial conjugation, wherein DNA is transferred between cells through direct contact, is highly prevalent in complex microbial communities and is responsible for spreading myriad genes related to human and environmental health. Despite their importance, much remains unknown regarding the mechanisms driving the spread and persistence of these plasmids in situ. Studies have demonstrated that transferring, acquiring, and maintaining a plasmid imposes a significant metabolic burden on the host. Simultaneously, emerging evidence suggests that the presence of a conjugative plasmid can also provide both obvious and unexpected benefits to their host and local community. Combined, this highlights a continuous cost-benefit tradeoff at the population level, likely contributing to overall plasmid abundance and long-term persistence. Yet, while the metabolic burdens of plasmid conjugation, and their causes, are widely studied, their attendant potential advantages are less clear. Here, we summarize current perspectives on conjugative plasmids' metabolic burden and then highlight the lesser-appreciated yet critical benefits that plasmid-mediated metabolic burdens may provide. We argue that this largely unexplored tradeoff is critical to both a fundamental theory of microbial populations and engineering applications and therefore warrants further detailed study.

Translation of the downstream ORF from bicistronic mRNAs by human cells: Impact of codon usage and splicing in the upstream ORF

Biochemistry textbooks describe eukaryotic mRNAs as monocistronic. However, increasing evidence reveals the widespread presence and translation of upstream open reading frames preceding the "main" ORF. DNA and RNA viruses infecting eukaryotes often produce polycistronic mRNAs and viruses have evolved multiple ways of manipulating the host's translation machinery. Here, we introduce an experimental model to study gene expression regulation from virus-like bicistronic mRNAs in human cells. The model consists of a short upstream ORF and a reporter downstream ORF encoding a fluorescent protein. We have engineered synonymous variants of the upstream ORF to explore large parameter space, including codon usage preferences, mRNA folding features, and splicing propensity. We show that human translation machinery can translate the downstream ORF from bicistronic mRNAs, albeit reporter protein levels are thousand times lower than those from the upstream ORF. Furthermore, synonymous recoding of the upstream ORF exclusively during elongation significantly influences its own translation efficiency, reveals cryptic splice signals, and modulates the probability of downstream ORF translation. Our results are consistent with a leaky scanning mechanism facilitating downstream ORF translation from bicistronic mRNAs in human cells, offering new insights into the role of upstream ORFs in translation regulation.

Improving the generalization of protein expression models with mechanistic sequence information

The growing demand for biological products drives many efforts to maximize expression of heterologous proteins. Advances in high-throughput sequencing can produce data suitable for building sequence-to-expression models with machine learning. The most accurate models have been trained on one-hot encodings, a mechanism-agnostic representation of nucleotide sequences. Moreover, studies have consistently shown that training on mechanistic sequence features leads to much poorer predictions, even with features that are known to correlate with expression, such as DNA sequence motifs, codon usage, or properties of mRNA secondary structures. However, despite their excellent local accuracy, current sequence-to-expression models can fail to generalize predictions far away from the training data. Through a comparative study across datasets in Escherichia coli and Saccharomyces cerevisiae, here we show that mechanistic sequence features can provide gains on model generalization, and thus improve their utility for predictive sequence design. We explore several strategies to integrate one-hot encodings and mechanistic features into a single predictive model, including feature stacking, ensemble model stacking, and geometric stacking, a novel architecture based on graph convolutional neural networks. Our work casts new light on mechanistic sequence features, underscoring the importance of domain-knowledge and feature engineering for accurate prediction of protein expression levels.

Codon usage analysis in selected virulence genes of Staphylococcal species

The Staphylococcus genus, composed of Gram-positive bacteria, includes several pathogenic species such as Staphylococcus aureus, S. epidermidis, S. haemolyticus, and S. saprophyticus, each implicated in a range of infections. This study investigates the codon usage patterns in key virulence genes, including Autolysin (alt), Elastin Binding protein (EbpS), Lipase, Thermonuclease, Intercellular Adhesion Protein (IcaR), and V8 Protease, across four Staphylococcus species. Using metrics such as the Effective Number of Codons (ENc), Relative Synonymous Codon Usage (RSCU), Codon Adaptation Index (CAI), alongside neutrality and parity plots, we explored the codon preferences and nucleotide composition biases. Our findings revealed a pronounced AT-rich codon preference, with AT-rich genomes likely aiding in energy-efficient translation and bacterial survival in host environments. These insights provide a deeper understanding of the evolutionary adaptations and translational efficiency mechanisms that contribute to the pathogenicity of Staphylococcus species. This knowledge could pave the way for novel therapeutic interventions targeting codon usage to disrupt virulence gene expression.

Engineered initiator tRNAs can effectively start translation at non-AUG start codons and diversify N-terminal amino acids for mRNA Display

mRNA display is an effective tool to identify high-affinity macrocyclic binders for challenging protein targets. The success of an mRNA display selection is dependent on generating highly diverse libraries with trillions of peptides. While translation elongation can canonically accommodate the 61 proteinogenic triplet codons, translation initiation is restricted to the native start codon AUG. Here, we investigate the ability of the Escherichia coli ribosome to initiate translation for 31 initiator tRNA (tRNAini) anticodon mutants at their respective cognate start codon using a NanoBiT translation assay. We show that the ability of those anticodon mutant tRNAsini to initiate translation is highly variable and is, in part, inhibited by tRNA misfolding induced by the anticodon mutations. We demonstrate based on two distinct misfolding patterns that translation efficiency can be effectively restored by introducing additional mutations that restore the active tRNA fold. We then used 10 of the engineered tRNAsini in a mutational analysis experiment for three reported macrocyclic peptides binding to Ubiquitin Specific Protease 7 (USP7). The observed enrichment of peptides correlates strongly with dissociation constants measured by surface plasmon resonance, and provides insights into the structure-activity relationship of the N-terminal amino acid without the requirement for peptide synthesis.

Predicting gene sequences with AI to study codon usage patterns

Selective pressure acts on the codon use, optimizing multiple, overlapping signals that are only partially understood. We trained AI models to predict codons given their amino acid sequence in the eukaryotes Saccharomyces cerevisiae and Schizosaccharomyces pombe and the bacteria Escherichia coli and Bacillus subtilis to study the extent to which we can learn patterns in naturally occurring codons to improve predictions. We trained our models on a subset of the proteins and evaluated their predictions on large, separate sets of proteins of varying lengths and expression levels. Our models significantly outperformed naïve frequency-based approaches, demonstrating that there are learnable dependencies in evolutionary-selected codon usage. The prediction accuracy advantage of our models is greater for highly expressed genes and is greater in bacteria than eukaryotes, supporting the hypothesis that there is a monotonic relationship between selective pressure for complex codon patterns and effective population size. In S. cerevisiae and bacteria, our models were more accurate for longer proteins, suggesting that the learned patterns may be related to cotranslational folding. Gene functionality and conservation were also important determinants that affect the performance of our models. Finally, we showed that using information encoded in homologous proteins has only a minor effect on prediction accuracy, perhaps due to complex codon-usage codes in genes undergoing rapid evolution. Our study employing contemporary AI methods offers a unique perspective and a deep-learning-based prediction tool for evolutionary-selected codons. We hope that these can be useful to optimize codon usage in endogenous and heterologous proteins.

Aminoglycoside tolerance in Vibrio cholerae engages translational reprogramming associated with queuosine tRNA modification

Tgt is the enzyme modifying the guanine (G) in tRNAs with GUN anticodon to queuosine (Q). tgt is required for optimal growth of Vibrio cholerae in the presence of sub-lethal aminoglycoside concentrations. We further explored here the role of the Q34 in the efficiency of codon decoding upon tobramycin exposure. We characterized its impact on the overall bacterial proteome, and elucidated the molecular mechanisms underlying the effects of Q34 modification in antibiotic translational stress response. Using molecular reporters, we showed that Q34 impacts the efficiency of decoding at tyrosine TAT and TAC codons. Proteomics analyses revealed that the anti-SoxR factor RsxA is better translated in the absence of tgt. RsxA displays a codon bias toward tyrosine TAT and overabundance of RsxA leads to decreased expression of genes belonging to SoxR oxidative stress regulon. We also identified conditions that regulate tgt expression. We propose that regulation of Q34 modification in response to environmental cues leads to translational reprogramming of transcripts bearing a biased tyrosine codon usage. In silico analysis further identified candidate genes which could be subject to such translational regulation, among which DNA repair factors. Such transcripts, fitting the definition of modification tunable transcripts, are central in the bacterial response to antibiotics.

Advances in recombinant protein production in microorganisms and functional peptide tags

Recombinant protein production in prokaryotic and eukaryotic cells is a fundamental technology for both research and industry. Achieving efficient protein synthesis is key to accelerating the discovery, characterization, and practical application of proteins. This review focuses on recent advances in recombinant protein production and strategies for more efficient protein production, especially using Escherichia coli and Saccharomyces cerevisiae. Additionally, this review summarizes the development of various functional peptide tags that can be employed for protein production, modification, and purification, including translation-enhancing peptide tags developed by our research group.

Association of R3HDM1 variants with growth and meat quality traits in Qinchuan cattle and its role in lipid accumulation

The R3H domain containing 1 (R3HDM1) gene has emerged as a candidate influencing residual feed intake and beef yield. Despite this, the genetic variation of R3HDM1 and its effects on beef cattle remain unexplored. This study identified four single nucleotide polymorphisms (SNPs) in the R3HDM1 gene of Qinchuan cattle, with the g.61695680 T > C SNP significantly associated with chest depth and backfat thickness. The g.61695680 T > C synonymous mutation significantly altered the RNA secondary structure and stability of R3HDM1. RNA interference experiments demonstrated that R3HDM1 knockdown reduced adipogenesis and lipid accumulation in bovine preadipocytes by modulating key adipogenic factors such as CEBPβ (P < 0.05), ACCα (P < 0.05), and ATGL (P < 0.01). These findings suggest that the g.61695680 T > C variants within R3HDM1 could serve as valuable molecular markers for selecting improved Qinchuan cattle, thus enhancing genetic selection strategies for beef production.

Influence of Nucleophosmin (NPM1) Genotypes on Outcome of Patients With AML: An AIEOP-BFM and COG-SWOG Intergroup Collaboration

PURPOSE

Several genomic subsets of NPM1 mutations with varying sequences (type A, B, D, etc) have been identified. Despite molecular heterogeneity, NPM1 mutations cumulatively portend a more favorable outcome, but biology and prognostic implications of different genomic subsets have not been extensively studied. In this multicentric study, we investigated the impact of NPM1 genotypes on patient's outcomes and interrogated the underlying biology of the different subtypes.

MATERIALS AND METHODS

Of more than 4,000 patients enrolled in multiple pediatric cooperative (AIEOP, BFM, ELAM02, NOPHO, DCOG, and COG trials), or adult (SWOG) trials, 348 pediatric and 75 adult AML patients with known NPM1 genotype and available outcome were selected for this study. Diverse NPM1 variants were correlated with the probabilities of overall survival (OS) and event-free survival. Nuclear localization and translational efficiency of the NPM1 variants was studied.

RESULTS

Evaluation of clinical outcome on the basis of NPM1 genotypes showed that patients with type A, B, and other rare variants had similarly favorable outcomes, whereas those with type D had a significantly worse outcome (OS of 63% for type D v 86% for type non-D, P = .005). Multivariate analysis confirmed type D as an independent prognostic factor associated with inferior OS (hazard ratio, 3; P = .005). In vitro, we demonstrated that in type D versus type A synonymous variants, codon optimality plays major roles in determining gene expression levels, and translation efficiency, which resulted in a more expressed NPM1-D mRNA and protein, mediating peculiar mitochondrial gene expression.

CONCLUSION

The evaluation of specific NPM1 genotypes identified AML patients with type D mutations being significantly associated with inferior outcomes, suggesting a reclassification of D cases to higher-risk groups.

A statistical-physics approach for codon usage optimisation

The concept of "codon optimisation" involves adjusting the coding sequence of a target protein to account for the inherent codon preferences of a host species and maximise protein expression in that species. However, there is still a lack of consensus on the most effective approach to achieve optimal results. Existing methods typically depend on heuristic combinations of different variables, leaving the user with the final choice of the sequence hit. In this study, we propose a new statistical-physics model for codon optimisation. This model, called the Nearest-Neighbour interaction (NN) model, links the probability of any given codon sequence to the "interactions" between neighbouring codons. We used the model to design codon sequences for different proteins of interest, and we compared our sequences with the predictions of some commercial tools. In order to assess the importance of the pair interactions, we additionally compared the NN model with a simpler method (Ind) that disregards interactions. It was observed that the NN method yielded similar Codon Adaptation Index (CAI) values to those obtained by other commercial algorithms, despite the fact that CAI was not explicitly considered in the algorithm. By utilising both the NN and Ind methods to optimise the reporter protein luciferase, and then analysing the translation performance in human cell lines and in a mouse model, we found that the NN approach yielded the highest protein expression in vivo. Consequently, we propose that the NN model may prove advantageous in biotechnological applications, such as heterologous protein expression or mRNA-based therapies.

Automated characterization and analysis of expression compatibility between regulatory sequences and metabolic genes in Escherichia coli

Utilizing standardized artificial regulatory sequences to fine-tuning the expression of multiple metabolic pathways/genes is a key strategy in the creation of efficient microbial cell factories. However, when regulatory sequence expression strengths are characterized using only a few reporter genes, they may not be applicable across diverse genes. This introduces great uncertainty into the precise regulation of multiple genes at multiple expression levels. To address this, our study adopted a fluorescent protein fusion strategy for a more accurate assessment of target protein expression levels. We combined 41 commonly-used metabolic genes with 15 regulatory sequences, yielding an expression dataset encompassing 520 unique combinations. This dataset highlighted substantial variation in protein expression level under identical regulatory sequences, with relative expression levels ranging from 2.8 to 176-fold. It also demonstrated that improving the strength of regulatory sequences does not necessarily lead to significant improvements in the expression levels of target proteins. Utilizing this dataset, we have developed various machine learning models and discovered that the integration of promoter regions, ribosome binding sites, and coding sequences significantly improves the accuracy of predicting protein expression levels, with a Spearman correlation coefficient of 0.72, where the promoter sequence exerts a predominant influence. Our study aims not only to provide a detailed guide for fine-tuning gene expression in the metabolic engineering of Escherichia coli but also to deepen our understanding of the compatibility issues between regulatory sequences and target genes.

Modeling and measuring how codon usage modulates the relationship between burden and yield during protein overexpression in bacteria

Excess utilization of translational resources is a critical source of burden on cells engineered to over-express exogenous proteins. To improve protein yields and genetic stability, researchers often use codon optimization strategies that improve translational efficiency by matching an exogenous gene's codon usage with that of the host organism's highly expressed genes. Despite empirical data that shows the benefits of codon optimization, little is known quantitatively about the relationship between codon usage bias and the burden imposed by protein overexpression. Here, we develop and experimentally evaluate a stochastic gene expression model that considers the impact of codon usage bias on the availability of ribosomes and different tRNAs in a cell. In agreement with other studies, our model shows that increasing exogenous protein expression decreases production of native cellular proteins in a linear fashion. We also find that the slope of this relationship is modulated by how well the codon usage bias of the exogenous gene and the host's genes match. Strikingly, we predict that an overoptimization domain exists where further increasing usage of optimal codons worsens yield and burden. We test our model by expressing sfGFP and mCherry2 from constructs that have a wide range of codon optimization levels in Escherichia coli . The results agree with our model, including for an mCherry2 gene sequence that appears to lose expression and genetic stability from codon overoptimization. Our findings can be leveraged by researchers to predict and design more optimal cellular systems through the use of more nuanced codon optimization strategies.

Versatile Dual Reporter to Identify Ribosome Pausing Motifs Alleviated by Translation Elongation Factor P

Protein synthesis is influenced by the chemical and structural properties of the amino acids incorporated into the polypeptide chain. Motifs containing consecutive prolines can slow the translation speed and cause ribosome stalling. Translation elongation factor P (EF-P) facilitates peptide bond formation in these motifs, thereby alleviating stalled ribosomes and restoring the regular translational speed. Ribosome pausing at various polyproline motifs has been intensively studied using a range of sophisticated techniques, including ribosome profiling, proteomics, and in vivo screening, with reporters incorporated into the chromosome. However, the full spectrum of motifs that cause translational pausing in Escherichia coli has not yet been identified. Here, we describe a plasmid-based dual reporter for rapid assessment of pausing motifs. This reporter contains two coupled genes encoding mScarlet-I and chloramphenicol acetyltransferase to screen motif libraries based on both bacterial fluorescence and survival. In combination with a diprolyl motif library, we used this reporter to reveal motifs of different pausing strengths in an E. coli strain lacking efp. Subsequently, we used the reporter for a high-throughput screen of four motif libraries, with and without prolines at different positions, sorted by fluorescence-associated cell sorting (FACS) and identify new motifs that influence the translational efficiency of the fluorophore. Our study provides an in vivo platform for rapid screening of amino acid motifs that affect translational efficiencies.

Genetic Code Expansion History and Modern Innovations

The genetic code is the foundation for all life. With few exceptions, the translation of nucleic acid messages into proteins follows conserved rules, which are defined by codons that specify each of the 20 proteinogenic amino acids. For decades, leading research groups have developed a catalogue of innovative approaches to extend nature's amino acid repertoire to include one or more noncanonical building blocks in a single protein. In this review, we summarize advances in the history of in vitro and in vivo genetic code expansion, and highlight recent innovations that increase the scope of biochemically accessible monomers and codons. We further summarize state-of-the-art knowledge in engineered cellular translation, as well as alterations to regulatory mechanisms that improve overall genetic code expansion. Finally, we distill existing limitations of these technologies into must-have improvements for the next generation of technologies, and speculate on future strategies that may be capable of overcoming current gaps in knowledge.

STALL-seq: mRNA-display selection of bacterial and eukaryotic translational arrest sequences from large random-sequence libraries

The translational arrest is a phenomenon wherein a temporary pause or slowing of the translation elongation reaction occurs due to the interaction between ribosome and nascent peptide. Recent studies have revealed that translational arrest peptides are involved in intracellular protein homeostasis regulatory functions, such as gene expression regulation at the translational level and regulation of cotranslational protein folding. Herein, we established a method for the large-scale in vitro selection of translational arrest peptides from DNA libraries by combining a modified mRNA display method and deep sequencing. We performed in vitro selection of translational arrest sequences from random-sequence libraries via mRNA display based on the Escherichia coli PURE system or wheat germ extract. Following several rounds of affinity selection, we obtained various candidate sequences that were not similar to known arrest peptides and subsequently confirmed their ribosome stalling activity by peptidyl-tRNA detection and toeprinting assay. Following the site-directed mutagenesis of the selected sequences, these clones were found to contain novel arrest peptide motifs. This method, termed STALL-seq (Selection of Translational Arrest sequences from Large Library sequencing), could be useful for the large-scale investigation of translational arrest sequences acting on both bacterial and eukaryotic ribosomes and could help discover novel intracellular regulatory mechanisms.

Vibrio natriegens: Application of a Fast-Growing Halophilic Bacterium

The fast growth accompanied with high substrate consumption rates and a versatile metabolism paved the way to exploit Vibrio natriegens as unconventional host for biotechnological applications. Meanwhile, a wealth of knowledge on the physiology, the metabolism, and the regulation in this halophilic marine bacterium has been gathered. Sophisticated genetic engineering tools and metabolic models are available and have been applied to engineer production strains and first chassis variants of V. natriegens. In this review, we update the current knowledge on the physiology and the progress in the development of synthetic biology tools and provide an overview of recent advances in metabolic engineering of this promising host. We further discuss future challenges to enhance the application range of V. natriegens.

Further Evidence for Strong Nonneutrality of Yeast Synonymous Mutations

Although synonymous mutations are commonly assumed neutral or nearly so, recent years have seen reports of fitness effects of synonymous mutations detected under laboratory conditions. In a previous study, we used genome editing to construct thousands of yeast mutants each carrying a synonymous or nonsynonymous mutation in one of 21 genes, and discovered that most synonymous and most nonsynonymous mutations are deleterious. A concern was raised that this observation could be caused by the fitness effects of potential CRISPR/Cas9 off-target edits and/or secondary mutations, and an experiment that would be refractory to such effects was proposed. Using genome sequencing, we here show that no CRISPR/Cas9 off-target editing occurred, although some mutants did carry secondary mutations. Analysis of mutants with negligible effects from secondary mutations and new data collected from the proposed experiment confirms the original conclusion. These findings, along with other reports of fitness effects of synonymous mutations from both case and systematic studies, necessitate a paradigm shift from assuming (near) neutrality of synonymous mutations.

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.

The expression of integron arrays is shaped by the translation rate of cassettes

Integrons are key elements in the rise and spread of multidrug resistance in Gram-negative bacteria. These genetic platforms capture cassettes containing promoterless genes and stockpile them in arrays of variable length. In the current integron model, expression of cassettes is granted by the Pc promoter in the platform and is assumed to decrease as a function of its distance. Here we explored this model using a large collection of 136 antibiotic resistance cassettes and show the effect of distance is in fact negligible. Instead, cassettes have a strong impact in the expression of downstream genes because their translation rate affects the stability of the whole polycistronic mRNA molecule. Hence, cassettes with reduced translation rates decrease the expression and resistance phenotype of cassettes downstream. Our data puts forward an integron model in which expression is contingent on the translation of cassettes upstream, rather than on the distance to the Pc.

Genetically-Encoded Fluorescence Barcodes for Single-Cell Analysis

Genetically-encoded, single-cell barcodes are broadly useful for experimental tasks such as lineage tracing or genetic screens. For such applications, a barcode library would ideally have high diversity (many unique barcodes), non-destructive identification (repeated measurements in the same cells or population), and fast, inexpensive readout (many cells and conditions). Current nucleic acid barcoding methods generate high diversity but require destructive and slow/expensive readout, and current fluorescence barcoding methods are non-destructive, fast, and inexpensive to readout but lack high diversity. We recently proposed theory for how fluorescent protein combinations may generate a high-diversity barcode library with non-destructive, fast and inexpensive identification. Here, we present an initial experimental proof-of-concept by generating a library of ~150 barcodes from two-way combinations of 18 fluorescent proteins. We use a pooled cloning strategy to generate a barcode library that is validated to contain every possible combination of the 18 fluorescent proteins. Experimental results using single mammalian cells and spectral flow cytometry demonstrate excellent classification performance of individual fluorescent proteins, with the exception of mTFP1, and of most evaluated barcodes, with many true positive rates >99%. The library is compatible with genetic screening for hundreds of genes (or gene pairs) and lineage tracing hundreds of clones. This work lays a foundation for greater diversity libraries (potentially ~105 and more) generated from hundreds of spectrally-resolvable tandem fluorescent protein probes.

Strategies to overcome the challenges of low or no expression of heterologous proteins in Escherichia coli

Protein expression is a critical process in diverse biological systems. For Escherichia coli, a widely employed microbial host in industrial catalysis and healthcare, researchers often face significant challenges in constructing recombinant expression systems. To maximize the potential of E. coli expression systems, it is essential to address problems regarding the low or absent production of certain target proteins. This article presents viable solutions to the main factors posing challenges to heterologous protein expression in E. coli, which includes protein toxicity, the intrinsic influence of gene sequences, and mRNA structure. These strategies include specialized approaches for managing toxic protein expression, addressing issues related to mRNA structure and codon bias, advanced codon optimization methodologies that consider multiple factors, and emerging optimization techniques facilitated by big data and machine learning.

Identification of RNA structures and their roles in RNA functions

The development of high-throughput RNA structure profiling methods in the past decade has greatly facilitated our ability to map and characterize different aspects of RNA structures transcriptome-wide in cell populations, single cells and single molecules. The resulting high-resolution data have provided insights into the static and dynamic nature of RNA structures, revealing their complexity as they perform their respective functions in the cell. In this Review, we discuss recent technical advances in the determination of RNA structures, and the roles of RNA structures in RNA biogenesis and functions, including in transcription, processing, translation, degradation, localization and RNA structure-dependent condensates. We also discuss the current understanding of how RNA structures could guide drug design for treating genetic diseases and battling pathogenic viruses, and highlight existing challenges and future directions in RNA structure research.

GenRCA: a user-friendly rare codon analysis tool for comprehensive evaluation of codon usage preferences based on coding sequences in genomes

BACKGROUND

The study of codon usage bias is important for understanding gene expression, evolution and gene design, providing critical insights into the molecular processes that govern the function and regulation of genes. Codon Usage Bias (CUB) indices are valuable metrics for understanding codon usage patterns across different organisms without extensive experiments. Considering that there is no one-fits-all index for all species, a comprehensive platform supporting the calculation and analysis of multiple CUB indices for codon optimization is greatly needed.

RESULTS

Here, we release GenRCA, an updated version of our previous Rare Codon Analysis Tool, as a free and user-friendly website for all-inclusive evaluation of codon usage preferences of coding sequences. In this study, we manually reviewed and implemented up to 31 codon preference indices, with 65 expression host organisms covered and batch processing of multiple gene sequences supported, aiming to improve the user experience and provide more comprehensive and efficient analysis.

CONCLUSIONS

Our website fills a gap in the availability of comprehensive tools for species-specific CUB calculations, enabling researchers to thoroughly assess the protein expression level based on a comprehensive list of 31 indices and further guide the codon optimization.

Integrating Deep Learning and Synthetic Biology: A Co-Design Approach for Enhancing Gene Expression via N-Terminal Coding Sequences

N-terminal coding sequence (NCS) influences gene expression by impacting the translation initiation rate. The NCS optimization problem is to find an NCS that maximizes gene expression. The problem is important in genetic engineering. However, current methods for NCS optimization such as rational design and statistics-guided approaches are labor-intensive yield only relatively small improvements. This paper introduces a deep learning/synthetic biology codesigned few-shot training workflow for NCS optimization. Our method utilizes k-nearest encoding followed by word2vec to encode the NCS, then performs feature extraction using attention mechanisms, before constructing a time-series network for predicting gene expression intensity, and finally a direct search algorithm identifies the optimal NCS with limited training data. We took green fluorescent protein (GFP) expressed by Bacillus subtilis as a reporting protein of NCSs, and employed the fluorescence enhancement factor as the metric of NCS optimization. Within just six iterative experiments, our model generated an NCS (MLD62) that increased average GFP expression by 5.41-fold, outperforming the state-of-the-art NCS designs. Extending our findings beyond GFP, we showed that our engineered NCS (MLD62) can effectively boost the production of N-acetylneuraminic acid by enhancing the expression of the crucial rate-limiting GNA1 gene, demonstrating its practical utility. We have open-sourced our NCS expression database and experimental procedures for public use.

Synonymous codon substitutions modulate transcription and translation of a divergent upstream gene by modulating antisense RNA production

Synonymous codons were originally viewed as interchangeable, with no phenotypic consequences. However, substantial evidence has now demonstrated that synonymous substitutions can perturb a variety of gene expression and protein homeostasis mechanisms, including translational efficiency, translational fidelity, and cotranslational folding of the encoded protein. To date, most studies of synonymous codon-derived perturbations have focused on effects within a single gene. Here, we show that synonymous codon substitutions made far within the coding sequence of Escherichia coli plasmid-encoded chloramphenicol acetyltransferase (cat) can significantly increase expression of the divergent upstream tetracycline resistance gene, tetR. In four out of nine synonymously recoded cat sequences tested, expression of the upstream tetR gene was significantly elevated due to transcription of a long antisense RNA (asRNA) originating from a transcription start site within cat. Surprisingly, transcription of this asRNA readily bypassed the native tet transcriptional repression mechanism. Even more surprisingly, accumulation of the TetR protein correlated with the level of asRNA, rather than total tetR RNA. These effects of synonymous codon substitutions on transcription and translation of a neighboring gene suggest that synonymous codon usage in bacteria may be under selection to both preserve the amino acid sequence of the encoded gene and avoid DNA sequence elements that can significantly perturb expression of neighboring genes. Avoiding such sequences may be especially important in plasmids and prokaryotic genomes, where genes and regulatory elements are often densely packed. Similar considerations may apply to the design of genetic circuits for synthetic biology applications.

Functional characterization of 2,832 JAG1 variants supports reclassification for Alagille syndrome and improves guidance for clinical variant interpretation

Pathogenic variants in the JAG1 gene are a primary cause of the multi-system disorder Alagille syndrome. Although variant detection rates are high for this disease, there is uncertainty associated with the classification of missense variants that leads to reduced diagnostic yield. Consequently, up to 85% of reported JAG1 missense variants have uncertain or conflicting classifications. We generated a library of 2,832 JAG1 nucleotide variants within exons 1-7, a region with a high number of reported missense variants, and designed a high-throughput assay to measure JAG1 membrane expression, a requirement for normal function. After calibration using a set of 175 known or predicted pathogenic and benign variants included within the variant library, 486 variants were characterized as functionally abnormal (n = 277 abnormal and n = 209 likely abnormal), of which 439 (90.3%) were missense. We identified divergent membrane expression occurring at specific residues, indicating that loss of the wild-type residue itself does not drive pathogenicity, a finding supported by structural modeling data and with broad implications for clinical variant classification both for Alagille syndrome and globally across other disease genes. Of 144 uncertain variants reported in patients undergoing clinical or research testing, 27 had functionally abnormal membrane expression, and inclusion of our data resulted in the reclassification of 26 to likely pathogenic. Functional evidence augments the classification of genomic variants, reducing uncertainty and improving diagnostics. Inclusion of this repository of functional evidence during JAG1 variant reclassification will significantly affect resolution of variant pathogenicity, making a critical impact on the molecular diagnosis of Alagille syndrome.

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.

Enrichment of rare codons at 5' ends of genes is a spandrel caused by evolutionary sequence turnover and does not improve translation

Previously, Tuller et al. found that the first 30-50 codons of the genes of yeast and other eukaryotes are slightly enriched for rare codons. They argued that this slowed translation, and was adaptive because it queued ribosomes to prevent collisions. Today, the translational speeds of different codons are known, and indeed rare codons are translated slowly. We re-examined this 5' slow translation 'ramp.' We confirm that 5' regions are slightly enriched for rare codons; in addition, they are depleted for downstream Start codons (which are fast), with both effects contributing to slow 5' translation. However, we also find that the 5' (and 3') ends of yeast genes are poorly conserved in evolution, suggesting that they are unstable and turnover relatively rapidly. When a new 5' end forms de novo, it is likely to include codons that would otherwise be rare. Because evolution has had a relatively short time to select against these codons, 5' ends are typically slightly enriched for rare, slow codons. Opposite to the expectation of Tuller et al., we show by direct experiment that genes with slowly translated codons at the 5' end are expressed relatively poorly, and that substituting faster synonymous codons improves expression. Direct experiment shows that slow codons do not prevent downstream ribosome collisions. Further informatic studies suggest that for natural genes, slow 5' ends are correlated with poor gene expression, opposite to the expectation of Tuller et al. Thus, we conclude that slow 5' translation is a 'spandrel'--a non-adaptive consequence of something else, in this case, the turnover of 5' ends in evolution, and it does not improve translation.

Escherichia coli as a versatile cell factory: Advances and challenges in recombinant protein production

E. coli plays a substantial role in recombinant protein production. Its importance increased with the discovery of recombinant DNA technology and the subsequent production of the first recombinant insulin in E. coli. E. coli is a widely used and cost-effective host to produce recombinant proteins. It is also noteworthy that a significant portion of the approved therapeutic proteins have been produced in this organism. Despite these advantages, it has some disadvantages, such as toxicity and lack of eukaryotic post-translational modifications that can lead to the production of misfolded, insoluble, or dysfunctional proteins. This study focused on the challenges and engineering approaches for improved expression and solubility in recombinant protein production in E. coli. In this context, solution strategies such as strain and vector selection, codon usage, mRNA stability, expression conditions, translocation to the periplasmic region and addition of fusion tags in E. coli were discussed.

Systems engineering Escherichia coli for efficient production p-coumaric acid from glucose

P-coumaric acid (p-CA), a pant metabolite with antioxidant and anti-inflammatory activity, is extensively utilized in biomedicine, food, and cosmetics industry. In this study, a synthetic pathway (PAL) for p-CA was designed, integrating three enzymes (AtPAL2, AtC4H, AtATR2) into a higher l-phenylalanine-producing strain Escherichia coli PHE05. However, the lower soluble expression and activity of AtC4H in the PAL pathway was a bottleneck for increasing p-CA titers. To overcome this limitation, the soluble expression of AtC4H was enhanced through N-terminal modifications. And an optimal mutant, AtC4HL373T/G211H, which exhibited a 4.3-fold higher kcat/Km value compared to the wild type, was developed. In addition, metabolic engineering strategies were employed to increase the intracellular NADPH pool. Overexpression of ppnk in engineered E. coli PHCA20 led to a 13.9-folds, 1.3-folds, and 29.1% in NADPH content, the NADPH/NADP+ ratio and p-CA titer, respectively. These optimizations significantly enhance p-CA production, in a 5-L fermenter using fed-batch fermentation, the p-CA titer, yield and productivity of engineered strain E. coli PHCA20 were 3.09 g/L, 20.01 mg/g glucose, and 49.05 mg/L/h, respectively. The results presented here provide a novel way to efficiently produce the plant metabolites using an industrial strain.

The Effects of Codon Usage on Protein Structure and Folding

The rate of protein synthesis is slower than many folding reactions and varies depending on the synonymous codons encoding the protein sequence. Synonymous codon substitutions thus have the potential to regulate cotranslational protein folding mechanisms, and a growing number of proteins have been identified with folding mechanisms sensitive to codon usage. Typically, these proteins have complex folding pathways and kinetically stable native structures. Kinetically stable proteins may fold only once over their lifetime, and thus, codon-mediated regulation of the pioneer round of protein folding can have a lasting impact. Supporting an important role for codon usage in folding, conserved patterns of codon usage appear in homologous gene families, hinting at selection. Despite these exciting developments, there remains few experimental methods capable of quantifying translation elongation rates and cotranslational folding mechanisms in the cell, which challenges the development of a predictive understanding of how biology uses codons to regulate protein folding.

Analysing the genetic code degeneracy: a consequence towards bacterial staining

As 20 naturally occurring amino acids are coded by 61 mRNA codons out of 64, it is obvious that 61→20 cannot have one-to-one mapping which generates the problem of codon degeneracy. Despite several efforts there is no specific outcome which can describe this well-known enigmatic degeneracy of the codon table. Since, every biological behaviour is regulated by protein which in turn consists of amino acids bearing the inherent characteristics of degeneracy among mRNA codons (Crick F.H.C. The Origin of the Genetic Code. J. Mol. Biol.1968; 38: 367-379), it is worthy to analyse the impact of such degeneracy on biological behaviours. Here, based on mathematical models using the concept of b-type of the nucleotide bases and hamming distances, an effort has been initiated to understand the impact of biasness of genetic code degeneracy on biological behaviours. The proposed models have been utilized to understand the characteristic features of bacterial genes of gram-positive and gram-negative bacteria. To the best of our knowledge, this is the first mathematical model to capture the effect of genetic code degeneracy, showing a paradigm towards understanding the behavioural difference between gram-positive and gram-negative bacteria, and thereby opening a new avenue for revealing differential biological properties.Communicated by Ramaswamy H. Sarma.

Selection on synonymous sites: the unwanted transcript hypothesis

Although translational selection to favour codons that match the most abundant tRNAs is not readily observed in humans, there is nonetheless selection in humans on synonymous mutations. We hypothesize that much of this synonymous site selection can be explained in terms of protection against unwanted RNAs - spurious transcripts, mis-spliced forms or RNAs derived from transposable elements or viruses. We propose not only that selection on synonymous sites functions to reduce the rate of creation of unwanted transcripts (for example, through selection on exonic splice enhancers and cryptic splice sites) but also that high-GC content (but low-CpG content), together with intron presence and position, is both particular to functional native mRNAs and used to recognize transcripts as native. In support of this hypothesis, transcription, nuclear export, liquid phase condensation and RNA degradation have all recently been shown to promote GC-rich transcripts and suppress AU/CpG-rich ones. With such 'traps' being set against AU/CpG-rich transcripts, the codon usage of native genes has, in turn, evolved to avoid such suppression. That parallel filters against AU/CpG-rich transcripts also affect the endosomal import of RNAs further supports the unwanted transcript hypothesis of synonymous site selection and explains the similar design rules that have enabled the successful use of transgenes and RNA vaccines.

An Integer Linear Programming Model to Optimize Coding DNA Sequences By Joint Control of Transcript Indicators

A Coding DNA Sequence (CDS) is a fraction of DNA whose nucleotides are grouped into consecutive triplets called codons, each one encoding an amino acid. Because most amino acids can be encoded by more than one codon, the same amino acid chain can be obtained by a very large number of different CDSs. These synonymous CDSs show different features that, also depending on the organism the transcript is expressed in, could affect translational efficiency and yield. The identification of optimal CDSs with respect to given transcript indicators is in general a challenging task, but it has been observed in recent literature that integer linear programming (ILP) can be a very flexible and efficient way to achieve it. In this article, we add evidence to this observation by proposing a new ILP model that simultaneously optimizes different well-grounded indicators. With this model, we efficiently find solutions that dominate those returned by six existing codon optimization heuristics.

Microdiversity of Salmonella Kentucky During Long-Term Colonization of a Dairy Herd

Salmonella enterica subsp. enterica serovar Kentucky was repeatedly isolated from a commercial dairy herd that was enrolled in a longitudinal study where feces of asymptomatic dairy cattle were sampled intensively over an 8-year period. The genomes of 5 Salmonella Kentucky isolates recovered from the farm 2 years before the onset of the long-term colonization event and 13 isolates collected during the period of endemicity were sequenced. A phylogenetic analysis inferred that the Salmonella Kentucky strains from the farm were distinct from poultry strains collected from the same region, and three subclades (K, A1, and A2) were identified among the farm isolates, each appearing at different times during the study. Based on the phylogenetic analysis, three separate lineages of highly similar Salmonella Kentucky were present in succession on the farm. Genomic heterogeneity between the clades helped identify regions, most notably transcriptional regulators, of the Salmonella Kentucky genome that may be involved in competition among highly similar strains. Notably, a region annotated as a hemolysin expression modulating protein (Hha) was identified in a putative plasmid region of strains that colonized a large portion of cows in the herd, suggesting that it may play a role in asymptomatic persistence within the bovine intestine. A cell culture assay of isolates from the three clades with bovine epithelial cells demonstrated a trend of decreased invasiveness of Salmonella Kentucky isolates over time, suggesting that clade-specific interactions with the animals on the farm may have played a role in the dynamics of strain succession. Results of this analysis further demonstrate an underappreciated level of genomic diversity within strains of the same Salmonella serovar, particularly those isolated during a long-term period of asymptomatic colonization within a single dairy herd.

Utilizing 5' UTR Engineering Enables Fine-Tuning of Multiple Genes within Operons to Balance Metabolic Flux in Bacillus subtilis

The application of synthetic biology tools to modulate gene expression to increase yield has been thoroughly demonstrated as an effective and convenient approach in industrial production. In this study, we employed a high-throughput screening strategy to identify a 5' UTR sequence from the genome of B. subtilis 168. This sequence resulted in a 5.8-fold increase in the expression level of EGFP. By utilizing the 5' UTR sequence to overexpress individual genes within the rib operon, it was determined that the genes ribD and ribAB serve as rate-limiting enzymes in the riboflavin synthesis pathway. Constructing a 5' UTR library to regulate EGFP expression resulted in a variation range in gene expression levels exceeding 100-fold. Employing the same 5' UTR library to regulate the expression of EGFP and mCherry within the operon led to a change in the expression ratio of these two genes by over 10,000-fold. So, employing a 5' UTR library to modulate the expression of the rib operon gene and construct a synthetic rib operon resulted in a 2.09-fold increase in riboflavin production. These results indicate that the 5' UTR sequence identified and characterized in this study can serve as a versatile synthetic biology toolkit for achieving complex metabolic network reconstruction. This toolkit can facilitate the fine-tuning of gene expression to produce target products.

Quantifying negative selection on synonymous variants

Widespread adoption of DNA sequencing has resulted in large numbers of genetic variants, whose contribution to disease is not easily determined. Although many types of variation are known to disrupt cellular processes in predictable ways, for some categories of variants, the effects may not be directly detectable. A particular example is synonymous variants, that is, those single-nucleotide variants that create a codon substitution, such that the produced amino acid sequence is unaffected. Contrary to the original theory suggesting that synonymous variants are benign, there is a growing volume of research showing that, despite their "silent" mechanism of action, some synonymous variation may be deleterious. Here, we studied the extent of the negative selective pressure acting on different classes of synonymous variants by analyzing the relative enrichment of synonymous singleton variants in the human exomes provided by gnomAD. Using a modification of the mutability-adjusted proportion of singletons (MAPS) metric as a measure of purifying selection, we found that some classes of synonymous variants are subject to stronger negative selection than others. For instance, variants that reduce codon optimality undergo stronger selection than optimality-increasing variants. Besides, selection affects synonymous variants implicated in splice-site-loss or splice-site-gain events. To understand what drives this negative selection, we tested a number of predictors in the aim to explain the variability in the selection scores. Our findings provide insights into the effects of synonymous variants at the population level, highlighting the specifics of the role that these variants play in health and disease.

Improvement of thermostability and catalytic efficiency of xylanase from Myceliophthora thermophilar by N-terminal and C-terminal truncation

Introduction

Extracting xylanase from thermophilic filamentous fungi is a feasible way to obtain xylanase with good thermal stability.

Methods

The transcriptomic data of Myceliophthora thermophilic destructive ATCC42464 were differentially expressed and enriched. By comparing the sequences of Mtxylan2 and more than 10 xylanases, the N-terminal and C-terminal of Mtxylan2 were truncated, and three mutants 28N, 28C and 28NC were constructed.

Results and discussion

GH11 xylan Mtxylan2 was identified by transcriptomic analysis, the specific enzyme activity of Mtxylan2 was 104.67 U/mg, and the optimal temperature was 65°C. Molecular modification of Mtxylan2 showed that the catalytic activity of the mutants was enhanced. Among them, the catalytic activity of 28C was increased by 9.3 times, the optimal temperature was increased by 5°C, and the residual enzyme activity remained above 80% after 30 min at 50-65°C, indicating that redundant C-terminal truncation can improve the thermal stability and catalytic performance of GH11 xylanase.

Differential Selection for Translation Efficiency Shapes Translation Machineries in Bacterial Species

Different bacterial species have dramatically different generation times, from 20-30 min in Escherichia coli to about two weeks in Mycobacterium leprae. The translation machinery in a cell needs to synthesize all proteins for a new cell in each generation. The three subprocesses of translation, i.e., initiation, elongation, and termination, are expected to be under stronger selection pressure to optimize in short-generation bacteria (SGB) such as Vibrio natriegens than in the long-generation Mycobacterium leprae. The initiation efficiency depends on the start codon decoded by the initiation tRNA, the optimal Shine-Dalgarno (SD) decoded by the anti-SD (aSD) sequence on small subunit rRNA, and the secondary structure that may embed the initiation signals and prevent them from being decoded. The elongation efficiency depends on the tRNA pool and codon usage. The termination efficiency in bacteria depends mainly on the nature of the stop codon and the nucleotide immediately downstream of the stop codon. By contrasting SGB with long-generation bacteria (LGB), we predict (1) SGB to have more ribosome RNA operons to produce ribosomes, and more tRNA genes for carrying amino acids to ribosomes, (2) SGB to have a higher percentage of genes using AUG as the start codon and UAA as the stop codon than LGB, (3) SGB to exhibit better codon and anticodon adaptation than LGB, and (4) SGB to have a weaker secondary structure near the translation initiation signals than LGB. These differences between SGB and LGB should be more pronounced in highly expressed genes than the rest of the genes. We present empirical evidence in support of these predictions.

Rational design of soluble expressed human aldehyde dehydrogenase 2 with high stability and activity in pepsin and trypsin

Acetaldehyde dehydrogenase 2 (ALDH2) is a crucial enzyme in alcohol metabolism, and oral administration of ALDH2 is a promising method for alcohol detoxification. However, recombinant ALDH2 is susceptible to hydrolysis by digestive enzymes in the gastrointestinal tract and is expressed as inactive inclusion bodies in E. coli. In this study, we performed three rounds of rational design to address these issues. Specifically, the surface digestive sites of pepsin and trypsin were replaced with other polar amino acids, while hydrophobic amino acids were incorporated to reshape the catalytic cavity of ALDH2. The resulting mutant DE2-852 exhibited a 45-fold increase in soluble expression levels, while its stability against trypsin and pepsin increased by eightfold and twofold, respectively. Its catalytic efficiency (kcat/Km) at pH 7.2 and 3.2 improved by more than four and five times, respectively, with increased Vmax and decreased Km values. The enhanced properties of DE2-852 were attributed to the D457Y mutation, which created a more compact protein structure and facilitated a faster collision between the substrate and catalytic residues. These results laid the foundation for the oral administration and mass preparation of highly active ALDH2 and offered insights into the oral application of other proteins.

A new look at the fluorescent protein-based approach for identifying optimal coding sequence for recombinant protein expression in E. coli

Due to the degeneracy of the genetic code, most amino acids are encoded by several codons. The choice among synonymous codons at the N-terminus of genes has a profound effect on protein expression in Escherichia coli. This is often explained by the different contributions of synonymous codons to mRNA secondary structure formation. Strong secondary structures at the 5'-end of mRNA interfere with ribosome binding and affect the process of translation initiation. In silico optimization of the gene 5'-end can significantly increase the level of protein expression; however, this method is not always effective due to the uncertainty of the exact mechanism by which synonymous substitutions affect expression; thus, it may produce nonoptimal variants as well as miss some of the best producers. In this paper, an alternative approach is proposed based on screening a partially randomized library of expression constructs comprising hundreds of selected synonymous variants. The effect of such substitutions was evaluated using the gene of interest fused to the reporter gene of the fluorescent protein with subsequent screening for the most promising candidates according to the reporter's signal intensity. The power of the approach is demonstrated by a significant increase in the prokaryotic expression of three proteins: canine cystatin C, human BCL2-associated athanogene 3 and human cardiac troponin I. This simple approach was suggested which may provide an efficient, easy, and inexpensive optimization method for poorly expressed proteins in bacteria.

Identification and characterization of an ene-reductase from Corynebacterium casei

The asymmetric reduction of α, β-unsaturated compounds conjugated with electron-withdrawing group by ene-reductases (ERs) is a valuable method for the synthesis of enantiopure chiral compounds. This study introduced an ER from Corynebacterium casei (CcER) which was heterologously expressed in Escherichia coli BL21(DE3), and the purified recombinant CcER was characterized for its biocatalytic properties. CcER exhibited the highest specific activity at 40 °C and pH 6.5, and showcased appreciable stability below 40 °C over a pH range of 6.0-7.0. The enzyme displayed high resistance to methanol. CcER accepted NADH or NADPH as a cofactor and exhibited a broad substrate spectrum towards α, β-unsaturated compounds. It achieved complete conversion of 2-cyclohexen-1-one and good performance for stereoselective reduction of (R)-carvone (conversion 98 %, diastereoselectivity 96 %). This study highlights the robustness and potential of CcER.

The TAM, a Translocation and Assembly Module for protein assembly and potential conduit for phospholipid transfer

The assembly of β-barrel proteins into the bacterial outer membrane is an essential process enabling the colonization of new environmental niches. The TAM was discovered as a module of the β-barrel protein assembly machinery; it is a heterodimeric complex composed of an outer membrane protein (TamA) bound to an inner membrane protein (TamB). The TAM spans the periplasm, providing a scaffold through the peptidoglycan layer and catalyzing the translocation and assembly of β-barrel proteins into the outer membrane. Recently, studies on another membrane protein (YhdP) have suggested that TamB might play a role in phospholipid transport to the outer membrane. Here we review and re-evaluate the literature covering the experimental studies on the TAM over the past decade, to reconcile what appear to be conflicting claims on the function of the TAM.

"Metabolic burden" explained: stress symptoms and its related responses induced by (over)expression of (heterologous) proteins in Escherichia coli

BACKGROUND

Engineering bacterial strains to redirect the metabolism towards the production of a specific product has enabled the development of industrial biotechnology. However, rewiring the metabolism can have severe implications for a microorganism, rendering cells with stress symptoms such as a decreased growth rate, impaired protein synthesis, genetic instability and an aberrant cell size. On an industrial scale, this is reflected in processes that are not economically viable.

MAIN TEXT

In literature, most stress symptoms are attributed to "metabolic burden", however the actual triggers and stress mechanisms involved are poorly understood. Therefore, in this literature review, we aimed to get a better insight in how metabolic engineering affects Escherichia coli and link the observed stress symptoms to its cause. Understanding the possible implications that chosen engineering strategies have, will help to guide the reader towards optimising the envisioned process more efficiently.

CONCLUSION

This review addresses the gap in literature and discusses the triggers and effects of stress mechanisms that can be activated when (over)expressing (heterologous) proteins in Escherichia coli. It uncovers that the activation of the different stress mechanisms is complex and that many are interconnected. The reader is shown that care has to be taken when (over)expressing (heterologous) proteins as the cell's metabolism is tightly regulated.