The optimization of mRNA expression level by its intrinsic properties—Insights from codon usage pattern and structural stability of mRNA

Molecular insights into codon usage analysis of mitochondrial fission and fusion gene: relevance to neurodegenerative diseases

Mitochondrial dysfunction is the leading cause of neurodegenerative disorders like Alzheimer's disease and Parkinson's disease. Mitochondria is a highly dynamic organelle continuously undergoing the process of fission and fusion for even distribution of components and maintaining proper shape, number, and bioenergetic functionality. A set of genes governs the process of fission and fusion. OPA1, Mfn1, and Mfn2 govern fusion, while Drp1, Fis1, MIEF1, and MIEF2 genes control fission. Determination of specific molecular patterns of transcripts of these genes revealed the impact of compositional constraints on selecting optimal codons. AGA and CCA codons were over-represented, and CCC, GTC, TTC, GGG, ACG were under-represented in the fusion gene set. In contrast, CTG was over-represented, and GCG, CCG, and TCG were under-represented in the fission gene set. Hydropathicity analysis revealed non-polar protein products of both fission and fusion gene set transcripts. AGA codon repeats are an integral part of translational regulation machinery and present a distinct pattern of over-representation and under-representation in different transcripts within the gene sets, suggestive of selective translational force precisely controlling the occurrence of the codon. Out of six synonymous codons, five synonymous codons encoding for leucine were used differently in both gene sets. Hence, forces regulating the occurrence of AGA and five synonymous leucine-encoding codons suggest translational selection. A correlation of mutational bias with gene expression and codon bias and GRAVY and AROMA signifies the selection pressure in both gene sets, while the correlation of compositional bias with gene expression, codon bias, protein properties, and minimum free energy signifies the presence of compositional constraints. More than 25% of codons of both gene sets showed a significant difference in codon usage. The overall analysis shed light on molecular features of gene sets involved in fission and fusion.

Can the Revolution in mRNA-Based Vaccine Technologies Solve the Intractable Health Issues of Current Ruminant Production Systems?

To achieve the World Health Organization's global Sustainable Development Goals, increased production of high-quality protein for human consumption is required while minimizing, ideally reducing, environmental impacts. One way to achieve these goals is to address losses within current livestock production systems. Infectious diseases are key limiters of edible protein production, affecting both quantity and quality. In addition, some of these diseases are zoonotic threats and potential contributors to the emergence of antimicrobial resistance. Vaccination has proven to be highly successful in controlling and even eliminating several livestock diseases of economic importance. However, many livestock diseases, both existing and emerging, have proven to be recalcitrant targets for conventional vaccination technologies. The threat posed by the COVID-19 pandemic resulted in unprecedented global investment in vaccine technologies to accelerate the development of safe and efficacious vaccines. While several vaccination platforms emerged as front runners to meet this challenge, the clear winner is mRNA-based vaccination. The challenge now is for livestock industries and relevant stakeholders to harness these rapid advances in vaccination to address key diseases affecting livestock production. This review examines the key features of mRNA vaccines, as this technology has the potential to control infectious diseases of importance to livestock production that have proven otherwise difficult to control using conventional approaches. This review focuses on the challenging diseases of ruminants due to their importance in global protein production. Overall, the current literature suggests that, while mRNA vaccines have the potential to address challenges in veterinary medicine, further developments are likely to be required for this promise to be realized for ruminant and other livestock species.

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.

Branched hydrophobic tails in lipid nanoparticles enhance mRNA delivery for cancer immunotherapy

Efficient and safe delivery of vulnerable mRNA is a long-standing challenge for the broad application of the emerging mRNA-based therapeutics. Herein, a combinatorial library containing 119 novel lipids was constructed via sequential aza-Michael addition reactions of arylates and varying amines to tackle the ongoing challenge in mRNA delivery. Through in vitro screening of the lipid library on IGROV 1 cells, we identified several synthetic lipids with superior mRNA delivery efficacy. The delivery capability of these lipids was verified by the potent expression of luciferase in BALB/c mice upon intravenous administration of luciferase-encoding mRNA lipid nanoparticles (LNPs). Further investigations on the structure-activity relationship revealed that lipids with branched hydrophobic tails were better at delivering mRNA than those containing linear tails at the similar total number of carbons. In comparison to linear tails, the branched tails endowed LNPs with less inner hydrophobicity, fewer surface charges, and proper stability, which benefits the cellular uptake of LNPs and the intracellular trafficking of mRNA, thus improves the delivery efficacy of mRNA. The therapeutical potential of the lead LNPs was evaluated by delivering ovalbumin (OVA)-encoding mRNA to mice bearing B16-OVA melanoma tumors. The results demonstrated that the administration of OVA mRNA LNPs significantly activated CD8+ T cells in tumor microenvironment and substantially prohibited the growth of the aggressive B16-OVA tumors. The robust antitumor efficacy highlights the great potential of these LNPs in cancer immunotherapy.

Genome-wide sequencing identifies a thermal-tolerance related synonymous mutation in the mussel, Mytilisepta virgata

The roles of synonymous mutations for adapting to stressful thermal environments are of fundamental biological and ecological interests but poorly understood. To study whether synonymous mutations influence thermal adaptation at specific microhabitats, a genome-wide genotype-phenotype association analysis is carried out in the black mussels Mytilisepta virgata. A synonymous mutation of Ubiquitin-specific Peptidase 15 (MvUSP15) is significantly associated with the physiological upper thermal limit. The individuals carrying GG genotype (the G-type) at the mutant locus possess significantly lower heat tolerance compared to the individuals carrying GA and AA genotypes (the A-type). When heated to sublethal temperature, the G-type exhibit higher inter-individual variations in MvUSP15 expression, especially for the mussels on the sun-exposed microhabitats. Taken together, a synonymous mutation in MvUSP15 can affect the gene expression profile and interact with microhabitat heterogeneity to influence thermal resistance. This integrative study sheds light on the ecological importance of adaptive synonymous mutations as an underappreciated genetic buffer against heat stress and emphasizes the importance of integrative studies at a microhabitat scale for evaluating and predicting the impacts of climate change.

Personalized pancreatic cancer therapy: from the perspective of mRNA vaccine

Pancreatic cancer is characterized by inter-tumoral and intra-tumoral heterogeneity, especially in genetic alteration and microenvironment. Conventional therapeutic strategies for pancreatic cancer usually suffer resistance, highlighting the necessity for personalized precise treatment. Cancer vaccines have become promising alternatives for pancreatic cancer treatment because of their multifaceted advantages including multiple targeting, minimal nonspecific effects, broad therapeutic window, low toxicity, and induction of persistent immunological memory. Multiple conventional vaccines based on the cells, microorganisms, exosomes, proteins, peptides, or DNA against pancreatic cancer have been developed; however, their overall efficacy remains unsatisfactory. Compared with these vaccine modalities, messager RNA (mRNA)-based vaccines offer technical and conceptional advances in personalized precise treatment, and thus represent a potentially cutting-edge option in novel therapeutic approaches for pancreatic cancer. This review summarizes the current progress on pancreatic cancer vaccines, highlights the superiority of mRNA vaccines over other conventional vaccines, and proposes the viable tactic for designing and applying personalized mRNA vaccines for the precise treatment of pancreatic cancer.

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.

Modulating Gene Expression within a Microbiome Based on Computational Models

Simple Summary

Development of computational biology methodologies has provided comprehensive understanding of the complexity of microbiomes, and the extensive ways in which they influence their environment. This has awakened a new research goal, aiming to not only understand the mechanisms in which microbiomes function, but to actively modulate and engineer them for various purposes. However, current microbiome engineering techniques are usually manually tailored for a specific system and neglect the different interactions between the new genetic information and the bacterial population, turning a blind eye to processes such as horizontal gene transfer, mutations, and other genetic alterations. In this work, we developed a generic computational method to automatically tune the expression of heterologous genes within a microbiome according to given preferences, to allow the functionality of the engineering process to propagate in longer periods of time. This goal was achieved by treating each part of the gene individually and considering long term fitness effects on the environment, providing computational and experimental evidence for this approach.

Abstract

Recent research in the field of bioinformatics and molecular biology has revealed the immense complexity and uniqueness of microbiomes, while also showcasing the impact of the symbiosis between a microbiome and its host or environment. A core property influencing this process is horizontal gene transfer between members of the bacterial community used to maintain genetic variation. The essential effect of this mechanism is the exposure of genetic information to a wide array of members of the community, creating an additional “layer” of information in the microbiome named the “plasmidome”. From an engineering perspective, introduction of genetic information to an environment must be facilitated into chosen species which will be able to carry out the desired effect instead of competing and inhibiting it. Moreover, this process of information transfer imposes concerns for the biosafety of genetic engineering of microbiomes as exposure of genetic information into unwanted hosts can have unprecedented ecological impacts. Current technologies are usually experimentally developed for a specific host/environment, and only deal with the transformation process itself at best, ignoring the impact of horizontal gene transfer and gene-microbiome interactions that occur over larger periods of time in uncontrolled environments. The goal of this research was to design new microbiome-specific versions of engineered genetic information, providing an additional layer of compatibility to existing engineering techniques. The engineering framework is entirely computational and is agnostic to the selected microbiome or gene by reducing the problem into the following set up: microbiome species can be defined as wanted or unwanted hosts of the modification. Then, every element related to gene expression (e.g., promoters, coding regions, etc.) and regulation is individually examined and engineered by novel algorithms to provide the defined expression preferences. Additionally, the synergistic effect of the combination of engineered gene blocks facilitates robustness to random mutations that might occur over time. This method has been validated using both computational and experimental tools, stemming from the research done in the iGEM 2021 competition, by the TAU group.

A novel silk fibroin protein-based fusion system for enhancing the expression of nanobodies in Escherichia coli

Nanobodies show a great potential in biomedical and biotechnology applications. Bacterial expression is the most widely used expression system for nanobody production. However, the yield of nanobodies is relatively low compared to that of eukaryotic systems. In this study, the repetitive amino acid sequence motifs (GAGAGS) found in silk fibroin protein (SFP) were developed as a novel fusion tag (SF-tag) to enhance the expression of nanobodies in Escherichia coli. SF-tags of 1 to 5 hexapeptide units were fused to the C-terminus of 4G8, a nanobody against human epididymis protein 4 (HE4). The protein yield of 4G8 variants was increased by the extension of hexapeptide units and achieved a 2.5 ~ 7.1-fold increase compared with that of untagged 4G8 (protein yield of 4G8-5C = 0.307 mg/g vs that of untagged 4G8 = 0.043 mg/g). Moreover, the fusion of SF-tags not only had no significant effect on the affinity of 4G8, but also showed a slight increase in the thermal stability of SF-tag-fused 4G8 variants. The fusion of SF-tags increased the transcription of 4G8 by 2.3 ~ 7.0-fold, indicating SF-tags enhanced the protein expression at the transcriptional level. To verify the applicability of the SF-tags for other nanobody expression, we further investigated the protein expression of two other anti-HE4 nanobodies 1G8 and 3A3 upon fusion with the SF-tags. Results indicated that the SF-tags enhanced the protein expression up to 5.2-fold and 5.7-fold for 1G8 and 3A3, respectively. For the first time, this study reported a novel and versatile fusion tag system based on the SFP for improving nanobody expression in Escherichia coli, which may enhance its potential for wider applications.Key points• A silk fibroin protein-based fusion tag (SF-tag) was developed to enhance the expression of nanobodies in Escherichia coli.• The SF-tag enhanced the nanobody expression at the transcriptional level.• The fusion of SF-tag had no significant effect on the affinity of nanobodies and could slightly increase the thermal stability of nanobodies.

Genome-wide analysis of codon usage in sesame (Sesamum indicum L.)

Sesamum indicum is an ancient oil crop grown in tropical and subtropical areas of the world. We have analyzed 23,538 coding sequences (CDS) of S. indicum to understand the factors shaping codon usage in this important oil crop plant. We identified eleven highly preferred codons in S. indicum that have AT-endings. The slope of a neutrality plot was less than one while effective number of codons (ENC) plot showed distribution above and below the standard curve. There is a significant relationship between protein length and relative synonymous codon usage (RSCU) at the primary axis while there is a weak correlation between protein length and Nc values. Correspondence analysis conducted on RSCU values differentiated CDS based on their GC content and their characteristic feature and showed a discrete distribution. Moreover, by determining codon usage, we found out that majority of the lignan biosynthesis related genes showed a weaker codon usage bias. These results provide insights into understanding codon evolution in sesame.

An Update on mRNA-Based Viral Vaccines

With the success of COVID-19 vaccines, newly created mRNA vaccines against other infectious diseases are beginning to emerge. Here, we review the structural elements required for designing mRNA vaccine constructs for effective in vitro synthetic transcription reactions. The unprecedently speedy development of mRNA vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was enabled with previous innovations in nucleoside modifications during in vitro transcription and lipid nanoparticle delivery materials of mRNA. Recent updates are briefly described in the status of mRNA vaccines against SARS-CoV-2, influenza virus, and other viral pathogens. Unique features of mRNA vaccine platforms and future perspectives are discussed.

Deciphering the co-adaptation of codon usage between respiratory coronaviruses and their human host uncovers candidate therapeutics for COVID-19

Coronavirus disease 2019 (COVID-19) has caused thousands of deaths worldwide and has become an urgent public health concern. The extraordinary interhuman transmission of this disease has urged scientists to examine the various facets of its pathogenic agent, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Herein, based on publicly available genomic data, we analyzed the codon usage co-adaptation profiles of SARS-CoV-2 and other respiratory coronaviruses (CoVs) with their human host, identified CoV-responsive human genes and their functional roles on the basis of both the relative synonymous codon usage (RSCU)-based correlation of viral genes with human genes and differential gene expression analysis, and predicted potential drugs for COVID-19 treatment based on these genes. The relatively high codon adaptation index (CAI) values (>0.70) signposted the gene expressivity efficiency of CoVs in human. The ENc-GC3 plot indicated that SARS-CoV-2 genome was under strict selection pressure while SARS-CoV and MERS-CoV were under selection and mutational pressures. The RSCU-based correlation analysis indicated that the viral genomes shared similar codons with a panoply of human genes. The merging of RSCU-based correlation data and SARS-CoV-2-responsive differentially expressed genes allowed the identification of human genes potentially affected by SARS-CoV-2 infection. Functional enrichment analysis indicated that these genes were enriched in biological processes and pathways related to host response to viral infection and immune response. Using the drug-gene interaction database, we screened a list of drugs that could target these genes as potential COVID-19 therapeutics. Our findings not only will contribute in vaccine development but also provide a useful set of drugs that could guide practitioners in strategical monitoring of COVID-19. We recommend practitioners to scrupulously screen this list of predicted drugs in order to authenticate those qualified for treating COVID-19 symptoms.

The combined influence of codon composition and tRNA copy number regulates translational efficiency by influencing synonymous nucleotide substitution

Codon usage bias is an important genomic phenomenon, where highly expressed genes use optimal codons for smoother translation with high yield, facilitated by the cognate tRNAs. Here, we presented the tRNA co-adaptation index (co-AI) by correlating tRNA gene copy number and codon composition in Saccharomyces cerevisiae. We observed that this co-AI is positively correlated with protein abundance and translation rate. Considering nucleotide substitutions, co-AI influences synonymous substitutions more than gene expression and protein abundance, the most important determinants of evolutionary rate. Co-AI correlates positively with mRNA secondary structure stability and mRNA half-life, which may lead to protein accumulation under high co-AI. However, the highly expressed proteins encoded by high co-AI genes are assisted by molecular chaperones to attain their proper functional conformation and prevent accumulation.

Comparative analysis of codon usage between Gossypium hirsutum and G. barbadense mitochondrial genomes

Gossypium hirsutum and G. barbadense mitochondrial genomes were analyzed to understand the factors shaping codon usage. While most analyses of codon usage suggest minimal to no bias, nucleotide composition, specifically GC content, was significantly correlated with codon usage. In general, both mitochondrial genomes favor codons that end in A or U, with a secondary preference for pyrimidine rich codons. These observations are similar to previous reports of codon usage in cotton nuclear genomes, possibly suggestive of a general bias spanning genomic compartment. Although evidence for codon usage bias is weak for most genes, we identified six genes (i.e. atp8, atp9, sdh3, sdh4, mttB and rpl2) with significant nonrandom codon usage. In general, we find multiple factors that influence cotton mitochondrial genome codon usage, which may include selection in a subset of genes.

The challenge and prospect of mRNA therapeutics landscape

Messenger RNA (mRNA)-based therapeutics hold the potential to cause a major revolution in the pharmaceutical industry because they can be used for precise and individualized therapy, and enable patients to produce therapeutic proteins in their own bodies without struggling with the comprehensive manufacturing issues associated with recombinant proteins. Compared with the current therapeutics, the production of mRNA is much cost-effective, faster and more flexible because it can be easily produced by in vitro transcription, and the process is independent of mRNA sequence. Moreover, mRNA vaccines allow people to develop personalized medications based on sequencing results and/or personalized conditions rapidly. Along with the great potential from bench to bedside, technical obstacles facing mRNA pharmaceuticals are also obvious. The stability, immunogenicity, translation efficiency, and delivery are all pivotal issues need to be addressed. In the recently published research results, these issues are gradually being overcome by state-of-the-art development technologies. In this review, we describe the structural properties and modification technologies of mRNA, summarize the latest advances in developing mRNA delivery systems, review the preclinical and clinical applications, and put forward our views on the prospect and challenges of developing mRNA into a new class of drug.

Saccharomyces cerevisiae: First Steps to a Suitable Model System To Study the Function and Intracellular Transport of Human Kidney Anion Exchanger 1

Distal renal tubular acidosis (dRTA) is a common kidney dysfunction characterized by impaired acid secretion via urine. Previous studies revealed that α-intercalated cells of dRTA patients express mutated forms of human kidney anion exchanger 1 (kAE1) which result in inefficient plasma membrane targeting or diminished expression levels of kAE1. However, the precise dRTA-causing processes are inadequately understood, and alternative model systems are helpful tools to address kAE1-related questions in a fast and inexpensive way. In contrast to a previous study, we successfully expressed full-length kAE1 in Saccharomyces cerevisiae. Using advanced microscopy techniques as well as different biochemical and functionality assays, plasma membrane localization and biological activity were confirmed for the heterologously expressed anion transporter. These findings represent first important steps to use the potential of yeast as a model organism for studying trafficking, activity, and degradation of kAE1 and its mutant variants in the future.

Saccharomyces cerevisiae has been frequently used to study biogenesis, functionality, and intracellular transport of various renal proteins, including ion channels, solute transporters, and aquaporins. Specific mutations in genes encoding most of these renal proteins affect kidney function in such a way that various disease phenotypes ultimately occur. In this context, human kidney anion exchanger 1 (kAE1) represents an important bicarbonate/chloride exchanger which maintains the acid-base homeostasis in the human body. Malfunctions in kAE1 lead to a pathological phenotype known as distal renal tubular acidosis (dRTA). Here, we evaluated the potential of baker's yeast as a model system to investigate different cellular aspects of kAE1 physiology. For the first time, we successfully expressed yeast codon-optimized full-length versions of tagged and untagged wild-type kAE1 and demonstrated their partial localization at the yeast plasma membrane (PM). Finally, pH and chloride measurements further suggest biological activity of full-length kAE1, emphasizing the potential of S. cerevisiae as a model system for studying trafficking, activity, and/or degradation of mammalian ion channels and transporters such as kAE1 in the future.

IMPORTANCE Distal renal tubular acidosis (dRTA) is a common kidney dysfunction characterized by impaired acid secretion via urine. Previous studies revealed that α-intercalated cells of dRTA patients express mutated forms of human kidney anion exchanger 1 (kAE1) which result in inefficient plasma membrane targeting or diminished expression levels of kAE1. However, the precise dRTA-causing processes are inadequately understood, and alternative model systems are helpful tools to address kAE1-related questions in a fast and inexpensive way. In contrast to a previous study, we successfully expressed full-length kAE1 in Saccharomyces cerevisiae. Using advanced microscopy techniques as well as different biochemical and functionality assays, plasma membrane localization and biological activity were confirmed for the heterologously expressed anion transporter. These findings represent first important steps to use the potential of yeast as a model organism for studying trafficking, activity, and degradation of kAE1 and its mutant variants in the future.

Improving the mutagenesis efficiency of the Kunkel method by codon optimization and annealing temperature adjustment

The Kunkel method is a widely used site-directed mutagenesis strategy that introduces point mutations by annealing mutation-containing oligonucleotides to single-stranded uracil-containing DNA (dU-ssDNA) templates. The method is fast and inexpensive and has been routinely employed to generate point mutations and multi-site mutations. However, its efficiency for point mutations is highly variable. In this work, codons in both DNA templates and mutagenic oligonucleotides were optimized to lower the GC percentage (GC%) of the complementary regions, and the oligonucleotide length was also extended to reduce the GC difference between upstream and downstream regions. These modifications largely increased the mutation efficiency of single-site mutagenesis. In addition, a multi-stage cooling programme was developed in the annealing step specifically for multi-site mutagenesis, which increased the simultaneous mutation efficiency. The modifications will help in generating antibody libraries by effectively randomizing multiple CDRs simultaneously.