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.

Strategies for improving the genome-editing efficiency of class 2 CRISPR/Cas system

Since its advent, gene-editing technology has been widely used in microorganisms, animals, plants, and other species. This technology shows remarkable application prospects, giving rise to a new biotechnological industry. In particular, third-generation gene editing technology, represented by the CRISPR/Cas9 system, has become the mainstream gene editing technology owing to its advantages of high efficiency, simple operation, and low cost. These systems can be widely used because they have been modified and optimized, leading to notable improvements in the efficiency of gene editing. This review introduces the characteristics of popular CRISPR/Cas systems and optimization methods aimed at improving the editing efficiency of class 2 CRISPR/Cas systems, providing a reference for the development of superior gene editing systems. Additionally, the review discusses the development and optimization of base editors, primer editors, gene activation and repression tools, as well as the advancement and refinement of compact systems such as IscB, TnpB, Fanzor, and Cas12f.

AntigenBoost: enhanced mRNA-based antigen expression through rational amino acid substitution

Messenger RNA (mRNA) vaccines represent a groundbreaking advancement in immunology and public health, particularly highlighted by their role in combating the COVID-19 pandemic. Optimizing mRNA-based antigen expression is a crucial focus in this emerging industry. We have developed a bioinformatics tool named AntigenBoost to address the challenge posed by destabilizing dipeptides that hinder ribosomal translation. AntigenBoost identifies these dipeptides within specific antigens and provides a range of potential amino acid substitution strategies using a two-dimensional scoring system. Through a combination of bioinformatics analysis and experimental validation, we significantly enhanced the in vitro expression of mRNA-derived Respiratory Syncytial Virus fusion glycoprotein and Influenza A Hemagglutinin antigen. Notably, a single amino acid substitution improved the immune response in mice, underscoring the effectiveness of AntigenBoost in mRNA vaccine design.

The paths toward non-viral CAR-T cell manufacturing: A comprehensive review of state-of-the-art methods

Although CAR-T cell therapy has emerged as a game-changer in cancer immunotherapy several bottlenecks limit its widespread use as a front-line therapy. Current protocols for the production of CAR-T cells rely mainly on the use of lentiviral/retroviral vectors. Nevertheless, according to the safety concerns around the use of viral vectors, there are several regulatory hurdles to their clinical use. Large-scale production of viral vectors under "Current Good Manufacturing Practice" (cGMP) involves rigorous quality control assessments and regulatory requirements that impose exorbitant costs on suppliers and as a result, lead to a significant increase in the cost of treatment. Pursuing an efficient non-viral method for genetic modification of immune cells is a hot topic in cell-based gene therapy. This study aims to investigate the current state-of-the-art in non-viral methods of CAR-T cell manufacturing. In the first part of this study, after reviewing the advantages and disadvantages of the clinical use of viral vectors, different non-viral vectors and the path of their clinical translation are discussed. These vectors include transposons (sleeping beauty, piggyBac, Tol2, and Tc Buster), programmable nucleases (ZFNs, TALENs, and CRISPR/Cas9), mRNA, plasmids, minicircles, and nanoplasmids. Afterward, various methods for efficient delivery of non-viral vectors into the cells are reviewed.

Recombinant multiepitope proteins expressed in Escherichia coli cells and their potential for immunodiagnosis

Recombinant multiepitope proteins (RMPs) are a promising alternative for application in diagnostic tests and, given their wide application in the most diverse diseases, this review article aims to survey the use of these antigens for diagnosis, as well as discuss the main points surrounding these antigens. RMPs usually consisting of linear, immunodominant, and phylogenetically conserved epitopes, has been applied in the experimental diagnosis of various human and animal diseases, such as leishmaniasis, brucellosis, cysticercosis, Chagas disease, hepatitis, leptospirosis, leprosy, filariasis, schistosomiasis, dengue, and COVID-19. The synthetic genes for these epitopes are joined to code a single RMP, either with spacers or fused, with different biochemical properties. The epitopes' high density within the RMPs contributes to a high degree of sensitivity and specificity. The RMPs can also sidestep the need for multiple peptide synthesis or multiple recombinant proteins, reducing costs and enhancing the standardization conditions for immunoassays. Methods such as bioinformatics and circular dichroism have been widely applied in the development of new RMPs, helping to guide their construction and better understand their structure. Several RMPs have been expressed, mainly using the Escherichia coli expression system, highlighting the importance of these cells in the biotechnological field. In fact, technological advances in this area, offering a wide range of different strains to be used, make these cells the most widely used expression platform. RMPs have been experimentally used to diagnose a broad range of illnesses in the laboratory, suggesting they could also be useful for accurate diagnoses commercially. On this point, the RMP method offers a tempting substitute for the production of promising antigens used to assemble commercial diagnostic kits.

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.

Heterologous protein production in filamentous fungi

Filamentous fungi are able to produce a wide range of valuable proteins and enzymes for many industrial applications. Recent advances in fungal genomics and experimental technologies are rapidly changing the approaches for the development and use of filamentous fungi as hosts for the production of both homologous and heterologous proteins. In this review, we highlight the benefits and challenges of using filamentous fungi for the production of heterologous proteins. We review various techniques commonly employed to improve the heterologous protein production in filamentous fungi, such as strong and inducible promoters, codon optimization, more efficient signal peptides for secretion, carrier proteins, engineering of glycosylation sites, regulation of the unfolded protein response and endoplasmic reticulum associated protein degradation, optimization of the intracellular transport process, regulation of unconventional protein secretion, and construction of protease-deficient strains. KEY POINTS: • This review updates the knowledge on heterologous protein production in filamentous fungi. • Several fungal cell factories and potential candidates are discussed. • Insights into improving heterologous gene expression are given.

Computational formulation of a multiepitope vaccine unveils an exceptional prophylactic candidate against Merkel cell polyomavirus

Merkel cell carcinoma (MCC) is a rare neuroendocrine skin malignancy caused by human Merkel cell polyomavirus (MCV), leading to the most aggressive skin cancer in humans. MCV has been identified in approximately 43%-100% of MCC cases, contributing to the highly aggressive nature of primary cutaneous carcinoma and leading to a notable mortality rate. Currently, no existing vaccines or drug candidates have shown efficacy in addressing the ailment caused by this specific pathogen. Therefore, this study aimed to design a novel multiepitope vaccine candidate against the virus using integrated immunoinformatics and vaccinomics approaches. Initially, the highest antigenic, immunogenic, and non-allergenic epitopes of cytotoxic T lymphocytes, helper T lymphocytes, and linear B lymphocytes corresponding to the virus whole protein sequences were identified and retrieved for vaccine construction. Subsequently, the selected epitopes were linked with appropriate linkers and added an adjuvant in front of the construct to enhance the immunogenicity of the vaccine candidates. Additionally, molecular docking and dynamics simulations identified strong and stable binding interactions between vaccine candidates and human Toll-like receptor 4. Furthermore, computer-aided immune simulation found the real-life-like immune response of vaccine candidates upon administration to the human body. Finally, codon optimization was conducted on the vaccine candidates to facilitate the in silico cloning of the vaccine into the pET28+(a) cloning vector. In conclusion, the vaccine candidate developed in this study is anticipated to augment the immune response in humans and effectively combat the virus. Nevertheless, it is imperative to conduct in vitro and in vivo assays to evaluate the efficacy of these vaccine candidates thoroughly. These evaluations will provide critical insights into the vaccine's effectiveness and potential for further development.

Research Advances on the Stability of mRNA Vaccines

Compared to other vaccines, the inherent properties of messenger RNA (mRNA) vaccines and their interaction with lipid nanoparticles make them considerably unstable throughout their life cycles, impacting their effectiveness and global accessibility. It is imperative to improve mRNA vaccine stability and investigate the factors influencing stability. Since mRNA structure, excipients, lipid nanoparticle (LNP) delivery systems, and manufacturing processes are the primary factors affecting mRNA vaccine stability, optimizing mRNA structure and screening excipients can effectively improve mRNA vaccine stability. Moreover, improving manufacturing processes could also prepare thermally stable mRNA vaccines with safety and efficacy. Here, we review the regulatory guidance associated with mRNA vaccine stability, summarize key factors affecting mRNA vaccine stability, and propose a possible research path to improve mRNA vaccine stability.

Analysis of 3.5 million SARS-CoV-2 sequences reveals unique mutational trends with consistent nucleotide and codon frequencies

BACKGROUND

Since the onset of the SARS-CoV-2 pandemic, bioinformatic analyses have been performed to understand the nucleotide and synonymous codon usage features and mutational patterns of the virus. However, comparatively few have attempted to perform such analyses on a considerably large cohort of viral genomes while organizing the plethora of available sequence data for a month-by-month analysis to observe changes over time. Here, we aimed to perform sequence composition and mutation analysis of SARS-CoV-2, separating sequences by gene, clade, and timepoints, and contrast the mutational profile of SARS-CoV-2 to other comparable RNA viruses.

METHODS

Using a cleaned, filtered, and pre-aligned dataset of over 3.5 million sequences downloaded from the GISAID database, we computed nucleotide and codon usage statistics, including calculation of relative synonymous codon usage values. We then calculated codon adaptation index (CAI) changes and a nonsynonymous/synonymous mutation ratio (dN/dS) over time for our dataset. Finally, we compiled information on the types of mutations occurring for SARS-CoV-2 and other comparable RNA viruses, and generated heatmaps showing codon and nucleotide composition at high entropy positions along the Spike sequence.

RESULTS

We show that nucleotide and codon usage metrics remain relatively consistent over the 32-month span, though there are significant differences between clades within each gene at various timepoints. CAI and dN/dS values vary substantially between different timepoints and different genes, with Spike gene on average showing both the highest CAI and dN/dS values. Mutational analysis showed that SARS-CoV-2 Spike has a higher proportion of nonsynonymous mutations than analogous genes in other RNA viruses, with nonsynonymous mutations outnumbering synonymous ones by up to 20:1. However, at several specific positions, synonymous mutations were overwhelmingly predominant.

CONCLUSIONS

Our multifaceted analysis covering both the composition and mutation signature of SARS-CoV-2 gives valuable insight into the nucleotide frequency and codon usage heterogeneity of SARS-CoV-2 over time, and its unique mutational profile compared to other RNA viruses.

A unifying network modeling approach for codon optimization

MOTIVATION

Synthesizing genes to be expressed in other organisms is an essential tool in biotechnology. While the many-to-one mapping from codons to amino acids makes the genetic code degenerate, codon usage in a particular organism is not random either. This bias in codon use may have a remarkable effect on the level of gene expression. A number of measures have been developed to quantify a given codon sequence's strength to express a gene in a host organism. Codon optimization aims to find a codon sequence that will optimize one or more of these measures. Efficient computational approaches are needed since the possible number of codon sequences grows exponentially as the number of amino acids increases.

RESULTS

We develop a unifying modeling approach for codon optimization. With our mathematical formulations based on graph/network representations of amino acid sequences, any combination of measures can be optimized in the same framework by finding a path satisfying additional limitations in an acyclic layered network. We tested our approach on bi-objectives commonly used in the literature, namely, Codon Pair Bias versus Codon Adaptation Index and Relative Codon Pair Bias versus Relative Codon Bias. However, our framework is general enough to handle any number of objectives concurrently with certain restrictions or preferences on the use of specific nucleotide sequences. We implemented our models using Python's Gurobi interface and showed the efficacy of our approach even for the largest proteins available. We also provided experimentation showing that highly expressed genes have objective values close to the optimized values in the bi-objective codon design problem.

AVAILABILITY AND IMPLEMENTATION

http://alpersen.bilkent.edu.tr/NetworkCodon.zip.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Past, Present, and Future of Non-Viral CAR T Cells

Adoptive transfer of chimeric antigen receptor (CAR) T lymphocytes is a powerful technology that has revolutionized the way we conceive immunotherapy. The impressive clinical results of complete and prolonged response in refractory and relapsed diseases have shifted the landscape of treatment for hematological malignancies, particularly those of lymphoid origin, and opens up new possibilities for the treatment of solid neoplasms. However, the widening use of cell therapy is hampered by the accessibility to viral vectors that are commonly used for T cell transfection. In the era of messenger RNA (mRNA) vaccines and CRISPR/Cas (clustered regularly interspaced short palindromic repeat-CRISPR-associated) precise genome editing, novel and virus-free methods for T cell engineering are emerging as a more versatile, flexible, and sustainable alternative for next-generation CAR T cell manufacturing. Here, we discuss how the use of non-viral vectors can address some of the limitations of the viral methods of gene transfer and allow us to deliver genetic information in a stable, effective and straightforward manner. In particular, we address the main transposon systems such as Sleeping Beauty (SB) and piggyBac (PB), the utilization of mRNA, and innovative approaches of nanotechnology like Lipid-based and Polymer-based DNA nanocarriers and nanovectors. We also describe the most relevant preclinical data that have recently led to the use of non-viral gene therapy in emerging clinical trials, and the related safety and efficacy aspects. We will also provide practical considerations for future trials to enable successful and safe cell therapy with non-viral methods for CAR T cell generation.

Frameshift and wild-type proteins are often highly similar because the genetic code and genomes were optimized for frameshift tolerance

Frameshift mutations have been considered of significant importance for the molecular evolution of proteins and their coding genes, while frameshift protein sequences encoded in the alternative reading frames of coding genes have been considered to be meaningless. However, functional frameshifts have been found widely existing. It was puzzling how a frameshift protein kept its structure and functionality while substantial changes occurred in its primary amino-acid sequence. This study shows that the similarities among frameshifts and wild types are higher than random similarities and are determined at different levels. Frameshift substitutions are more conservative than random substitutions in the standard genetic code (SGC). The frameshift substitutions score of SGC ranks in the top 2.0-3.5% of alternative genetic codes, showing that SGC is nearly optimal for frameshift tolerance. In many genes and certain genomes, frameshift-resistant codons and codon pairs appear more frequently than expected, suggesting that frameshift tolerance is achieved through not only the optimality of the genetic code but, more importantly, the further optimization of a specific gene or genome through the usages of codons/codon pairs, which sheds light on the role of frameshift mutations in molecular and genomic evolution.

Advances in COVID-19 mRNA vaccine development

To date, the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has determined 399,600,607 cases and 5,757,562 deaths worldwide. COVID-19 is a serious threat to human health globally. The World Health Organization (WHO) has declared COVID-19 pandemic a major public health emergency. Vaccination is the most effective and economical intervention for controlling the spread of epidemics, and consequently saving lives and protecting the health of the population. Various techniques have been employed in the development of COVID-19 vaccines. Among these, the COVID-19 messenger RNA (mRNA) vaccine has been drawing increasing attention owing to its great application prospects and advantages, which include short development cycle, easy industrialization, simple production process, flexibility to respond to new variants, and the capacity to induce better immune response. This review summarizes current knowledge on the structural characteristics, antigen design strategies, delivery systems, industrialization potential, quality control, latest clinical trials and real-world data of COVID-19 mRNA vaccines as well as mRNA technology. Current challenges and future directions in the development of preventive mRNA vaccines for major infectious diseases are also discussed.

Codon usage bias

Codon usage bias is the preferential or non-random use of synonymous codons, a ubiquitous phenomenon observed in bacteria, plants and animals. Different species have consistent and characteristic codon biases. Codon bias varies not only with species, family or group within kingdom, but also between the genes within an organism. Codon usage bias has evolved through mutation, natural selection, and genetic drift in various organisms. Genome composition, GC content, expression level and length of genes, position and context of codons in the genes, recombination rates, mRNA folding, and tRNA abundance and interactions are some factors influencing codon bias. The factors shaping codon bias may also be involved in evolution of the universal genetic code. Codon-usage bias is critical factor determining gene expression and cellular function by influencing diverse processes such as RNA processing, protein translation and protein folding. Codon usage bias reflects the origin, mutation patterns and evolution of the species or genes. Investigations of codon bias patterns in genomes can reveal phylogenetic relationships between organisms, horizontal gene transfers, molecular evolution of genes and identify selective forces that drive their evolution. Most important application of codon bias analysis is in the design of transgenes, to increase gene expression levels through codon optimization, for development of transgenic crops. The review gives an overview of deviations of genetic code, factors influencing codon usage or bias, codon usage bias of nuclear and organellar genes, computational methods to determine codon usage and the significance as well as applications of codon usage analysis in biological research, with emphasis on plants.

Exploring Codon Adjustment Strategies towards Escherichia coli-Based Production of Viral Proteins Encoded by HTH1, a Novel Prophage of the Marine Bacterium Hypnocyclicus thermotrophus

Marine viral sequence space is immense and presents a promising resource for the discovery of new enzymes interesting for research and biotechnology. However, bottlenecks in the functional annotation of viral genes and soluble heterologous production of proteins hinder access to downstream characterization, subsequently impeding the discovery process. While commonly utilized for the heterologous expression of prokaryotic genes, codon adjustment approaches have not been fully explored for viral genes. Herein, the sequence-based identification of a putative prophage is reported from within the genome of Hypnocyclicus thermotrophus, a Gram-negative, moderately thermophilic bacterium isolated from the Seven Sisters hydrothermal vent field. A prophage-associated gene cluster, consisting of 46 protein coding genes, was identified and given the proposed name Hypnocyclicus thermotrophus phage H1 (HTH1). HTH1 was taxonomically assigned to the viral family Siphoviridae, by lowest common ancestor analysis of its genome and phylogeny analyses based on proteins predicted as holin and DNA polymerase. The gene neighbourhood around the HTH1 lytic cassette was found most similar to viruses infecting Gram-positive bacteria. In the HTH1 lytic cassette, an N-acetylmuramoyl-L-alanine amidase (Amidase_2) with a peptidoglycan binding motif (LysM) was identified. A total of nine genes coding for enzymes putatively related to lysis, nucleic acid modification and of unknown function were subjected to heterologous expression in Escherichia coli. Codon optimization and codon harmonization approaches were applied in parallel to compare their effects on produced proteins. Comparison of protein yields and thermostability demonstrated that codon optimization yielded higher levels of soluble protein, but codon harmonization led to proteins with higher thermostability, implying a higher folding quality. Altogether, our study suggests that both codon optimization and codon harmonization are valuable approaches for successful heterologous expression of viral genes in E. coli, but codon harmonization may be preferable in obtaining recombinant viral proteins of higher folding quality.

Molecular biology interventions for activity improvement and production of industrial enzymes

Metagenomics and directed evolution technology have brought a revolution in search of novel enzymes from extreme environment and improvement of existing enzymes and tuning them towards certain desired properties. Using advanced tools of molecular biology i.e. next generation sequencing, site directed mutagenesis, fusion protein, surface display, etc. now researchers can engineer enzymes for improved activity, stability, and substrate specificity to meet the industrial demand. Although many enzymatic processes have been developed up to industrial scale, still there is a need to overcome limitations of maintaining activity during the catalytic process. In this article recent developments in enzymes industrial applications and advancements in metabolic engineering approaches to improve enzymes efficacy and production are reviewed.