Design Of Synthetic Genes Research Articles

Codon optimality influences homeostatic gene expression in zebrafish.

The ribosome plays a crucial role in translating mRNA into protein; however, the genetic code extends beyond merely specifying amino acids. Upon translation, codons, the three-nucleotide sequences interpreted by ribosomes, have regulatory properties affecting mRNA stability, a phenomenon known as codon optimality. Codon optimality has been previously observed in vertebrates during embryogenesis, where specific codons can influence the stability and degradation rates of mRNA transcripts. In our previous work, we demonstrated that codon optimality impacts mRNA stability in human cell lines. However, the extent to which codon content influences vertebrate gene expression in vivo remained unclear. In this study, we expand on our previous findings by demonstrating that codon optimality has a robust effect on homeostatic mRNA and protein levels in whole zebrafish during normal physiological conditions. Using reporters with nearly identical nucleotide sequences but different codon compositions, all expressed from the same genomic locus, we show that codon composition can significantly influence gene expression. This study provides new insights into the regulatory roles of codon usage in vertebrate gene expression and underscores the importance of considering codon optimality in genetic and translational research. These findings have broad implications for understanding the complexities of gene regulation and could inform the design of synthetic genes and therapeutic strategies targeting mRNA stability.

Using protein-per-mRNA differences among human tissues in codon optimization

BackgroundCodon usage and nucleotide composition of coding sequences have profound effects on protein expression. However, while it is recognized that different tissues have distinct tRNA profiles and codon usages in their transcriptomes, the effect of tissue-specific codon optimality on protein synthesis remains elusive.ResultsWe leverage existing state-of-the-art transcriptomics and proteomics datasets from the GTEx project and the Human Protein Atlas to compute the protein-to-mRNA ratios of 36 human tissues. Using this as a proxy of translational efficiency, we build a machine learning model that identifies codons enriched or depleted in specific tissues. We detect two clusters of tissues with an opposite pattern of codon preferences. We then use these identified patterns for the development of CUSTOM, a codon optimizer algorithm which suggests a synonymous codon design in order to optimize protein production in a tissue-specific manner. In human cell-line models, we provide evidence that codon optimization should take into account particularities of the translational machinery of the tissues in which the target proteins are expressed and that our approach can design genes with tissue-optimized expression profiles.ConclusionsWe provide proof-of-concept evidence that codon preferences exist in tissue-specific protein synthesis and demonstrate its application to synthetic gene design. We show that CUSTOM can be of benefit in biological and biotechnological applications, such as in the design of tissue-targeted therapies and vaccines.

Revealing determinants of translation efficiency via whole-gene codon randomization and machine learning.

It has beenknown for decades that codon usage contributes to translation efficiency and hence to protein production levels. However, its role in protein synthesis is still only partly understood. This lack of understanding hampers the design of synthetic genes for efficient protein production. In this study, we generated a synonymous codon-randomized library of the complete coding sequence of red fluorescent protein. Protein production levels and the full coding sequences were determined for 1459 gene variants in Escherichia coli. Using different machine learning approaches, these data wereused to reveal correlations between codon usage and protein production. Interestingly, protein production levels can be relatively accurately predicted (Pearson correlation of 0.762) by a Random Forest model that only relies on the sequence information ofthe first eight codons. In this region, close tothe translation initiation site, mRNA secondary structure rather than Codon Adaptation Index (CAI)is the key determinant of protein production. This study clearly demonstratesthe key role of codons at the start of the coding sequence. Furthermore, these results imply that commonly used CAI-based codon optimization of the full coding sequence is not a very effective strategy. One should rather focus on optimizing protein production via reducing mRNA secondary structure formation with the first few codons.

Optimized rapid production of recombinant secreted proteins in CHO cells grown in suspension: The case of RBD.

Chinese Hamster Ovary cells (CHO) have become the most common workhorse for the commercial production of therapeutic proteins, as well as for the production of recombinant proteins for biomedical research. The ability to grow at high density in suspension, the adaptability to serum free media, and the ease transfection and scale up, made CHO cell line highly productive and robust for large-scale production. Here, we present an optimized workflow used to successfully express and purify a number of human proteins with a yield up to 5mg/L of culture. The entire protocol, from the synthetic gene design to the assessment of purified protein quality, can be completed in 2 weeks. The established cell culture platform has been efficiently adapted to rapidly produce the receptor-binding domain (RBD) in SARS-CoV-2 S protein, a protein required by many laboratories in 2020 to better understand the initial step of infection related to COVID-19 pandemic. An overall yield of 2mg of high quality soluble RBD per liter of culture was obtained, a production 10-times cheaper than commercial preparations, this representing an intriguing strategy for future challenges.

An Integrated Algorithm for Designing Oligodeoxynucleotides for Gene Synthesis.

The design and construction of large synthetic genes can be a slow, difficult, and confusing process, especially in the key step of oligodeoxynucleotide design. Herein we present an integrated algorithm to design oligonucleotide sets for gene synthesis by both ligase chain reaction and polymerase chain reaction. It offers much flexibility with no constraints on the gene to be synthesized. Firstly, it divides the long-input DNA sequence by a greedy algorithm based on the length of the oligodeoxynucleotide overlap region. Secondly, it tunes the length of the overlap region iteratively in an attempt to minimize the melting temperature variance of overlap. Thirdly, dynamic programming algorithm is used to achieve the uniform melting temperature of the oligodeoxynucleotide overlaps. Finally, the oligodeoxynucleotides with homologous melting temperature necessary for ligase chain reaction-based or two-step assembly PCR-based synthesis of the desired gene are outputted.

Codon pair optimization (CPO): a software tool for synthetic gene design based on codon pair bias to improve the expression of recombinant proteins in Pichia pastoris

BackgroundCodon optimization is a common method to improve protein expression levels in Pichia pastoris and the current strategy is to replace rare codons with preferred codons to match the codon usage bias. However, codon-pair contexts have a profound effect on translation efficiency by influencing both translational elongation rates and accuracy. Until now, it remains untested whether optimized genes based on codon pair bias results in higher protein expression levels compared to codon usage bias.ResultsIn this study, an algorithm based on dynamic programming was introduced to develop codon pair optimization (CPO) which is a software tool to provide simple and efficient codon pair optimization for synthetic gene design in Pichia pastoris. Two reporters (MT1-MMP E2C6 and ADAM17 A9B8 scFvs) were employed to test the effects of codon pair bias and CPO optimization on their protein expression levels. Four variants of MT1-MMP E2C6 and ADAM17 A9B8 for each were generated, one variant with the best codon-pair context, one with the worst codon-pair context, one with unbiased codon-pair context, and another optimized based on codon usage. The expression levels of variants with the worst codon-pair context were almost undetectable by Western blot and the variants with the best codon-pair context were expressed well. The expression levels on MT1-MMP E2C6 and ADAM17 A9B8 were more than five times and seven times higher in the optimized sequences based on codon-pair context compared to that based on codon usage, respectively. The results indicated that the codon-pair context-based codon optimization is more effective in enhancing expression of protein in Pichia pastoris.ConclusionsCodon-pair context plays an important role on the protein expression in Pichia pastoris. The codon pair optimization (CPO) software developed in this study efficiently improved the protein expression levels of exogenous genes in Pichia pastoris, suggesting gene design based on codon pair bias is an alternative strategy for high expression of recombinant proteins in Pichia pastoris.

Trans-species synthetic gene design allows resistance pyramiding and broad-spectrum engineering of virus resistance in plants.

SummaryTo infect plants, viruses rely heavily on their host's machinery. Plant genetic resistances based on host factor modifications can be found among existing natural variability and are widely used for some but not all crops. While biotechnology can supply for the lack of natural resistance alleles, new strategies need to be developed to increase resistance spectra and durability without impairing plant development. Here, we assess how the targeted allele modification of the Arabidopsis thaliana translation initiation factor eIF4E1 can lead to broad and efficient resistance to the major group of potyviruses. A synthetic Arabidopsis thaliana eIF4E1 allele was designed by introducing multiple amino acid changes associated with resistance to potyvirus in naturally occurring Pisum sativum alleles. This new allele encodes a functional protein while maintaining plant resistance to a potyvirus isolate that usually hijacks eIF4E1. Due to its biological functionality, this synthetic allele allows, at no developmental cost, the pyramiding of resistances to potyviruses that selectively use the two major translation initiation factors, eIF4E1 or its isoform eIFiso4E. Moreover, this combination extends the resistance spectrum to potyvirus isolates for which no efficient resistance has so far been found, including resistance‐breaking isolates and an unrelated virus belonging to the Luteoviridae family. This study is a proof‐of‐concept for the efficiency of gene engineering combined with knowledge of natural variation to generate trans‐species virus resistance at no developmental cost to the plant. This has implications for breeding of crops with broad‐spectrum and high durability resistance using recent genome editing techniques.

Gene design, fusion technology and TEV cleavage conditions influence the purification of oxidized disulphide-rich venom peptides in Escherichia coli

BackgroundAnimal venoms are large, complex libraries of bioactive, disulphide-rich peptides. These peptides, and their novel biological activities, are of increasing pharmacological and therapeutic importance. However, recombinant expression of venom peptides in Escherichia coli remains difficult due to the significant number of cysteine residues requiring effective post-translational processing. There is also an urgent need to develop high-throughput recombinant protocols applicable to the production of reticulated peptides to enable efficient screening of their drug potential. Here, a comprehensive study was developed to investigate how synthetic gene design, choice of fusion tag, compartment of expression, tag removal conditions and protease recognition site affect levels of solubility of oxidized venom peptides produced in E. coli.ResultsThe data revealed that expression of venom peptides imposes significant pressure on cysteine codon selection. DsbC was the best fusion tag for venom peptide expression, in particular when the fusion was directed to the bacterial periplasm. While the redox activity of DsbC was not essential to maximize expression of recombinant fusion proteins, redox activity did lead to higher levels of correctly folded target peptides. With the exception of proline, the canonical TEV protease recognition site tolerated all other residues at its C-terminus, confirming that no non-native residues, which might affect activity, need to be incorporated at the N-terminus of recombinant peptides for tag removal.ConclusionsThis study reveals that E. coli is a convenient heterologous host for the expression of soluble and functional venom peptides. Using the optimal construct design, a large and diverse range of animal venom peptides were produced in the µM scale. These results open up new possibilities for the high-throughput production of recombinant disulphide-rich peptides in E. coli.

Synthetic gene design—The rationale for codon optimization and implications for molecular pharming in plants

Degeneracy in the genetic code allows multiple codon sequences to encode the same protein. Codon usage bias in genes is the term given to the preferred use of particular synonymous codons. Synonymous codon substitutions had been regarded as "silent" as the primary structure of the protein was not affected; however, it is now accepted that synonymous substitutions can have a significant effect on heterologous protein expression. Codon optimization, the process of altering codons within the gene sequence to improve recombinant protein expression, has become widely practised. Multiple inter-linked factors affecting protein expression need to be taken into consideration when optimizing a gene sequence. Over the years, various computer programmes have been developed to aid in the gene sequence optimization process. However, as the rulebook for altering codon usage to affect protein expression is still not completely understood, it is difficult to predict which strategy, if any, will design the "optimal" gene sequence. In this review, codon usage bias and factors affecting codon selection will be discussed and the evidence for codon optimization impact will be reviewed for recombinant protein expression using plants as a case study. These developments will be relevant to all recombinant expression systems; however, molecular pharming in plants is an area which has consistently encountered difficulties with low levels of recombinant protein expression, and should benefit from an evidence based rational approach to synthetic gene design. Biotechnol. Bioeng. 2017;114: 492-502. © 2016 Wiley Periodicals, Inc.

Synthetic Gene Design Using Codon Optimization On-Line (COOL).

Codon optimization has been widely used for designing native or synthetic genes to enhance their expression in heterologous host organisms. We recently developed Codon Optimization On-Line (COOL) which is a web-based tool to provide multi-objective codon optimization functionality for synthetic gene design. COOL provides a simple and flexible interface for customizing codon optimization based on several design parameters such as individual codon usage, codon pairing, and codon adaptation index. User-defined sequences can also be compared against the COOL optimized ones to show the extent by which the user's sequences can be evaluated and further improved. The utility of COOL is demonstrated via a case study where the codon optimized sequence of an invertase enzyme is generated for the enhanced expression in E. coli.

Codon Context Optimization in Synthetic Gene Design.

Advances in de novo synthesis of DNA and computational gene design methods make possible the customization of genes by direct manipulation of features such as codon bias and mRNA secondary structure. Codon context is another feature significantly affecting mRNA translational efficiency, but existing methods and tools for evaluating and designing novel optimized protein coding sequences utilize untested heuristics and do not provide quantifiable guarantees on design quality. In this study we examine statistical properties of codon context measures in an effort to better understand the phenomenon. We analyze the computational complexity of codon context optimization and design exact and efficient heuristic gene recoding algorithms under reasonable constraint models. We also present a web-based tool for evaluating codon context bias in the appropriate context.

The Selective Advantage of Synonymous Codon Usage Bias in Salmonella.

The genetic code in mRNA is redundant, with 61 sense codons translated into 20 different amino acids. Individual amino acids are encoded by up to six different codons but within codon families some are used more frequently than others. This phenomenon is referred to as synonymous codon usage bias. The genomes of free-living unicellular organisms such as bacteria have an extreme codon usage bias and the degree of bias differs between genes within the same genome. The strong positive correlation between codon usage bias and gene expression levels in many microorganisms is attributed to selection for translational efficiency. However, this putative selective advantage has never been measured in bacteria and theoretical estimates vary widely. By systematically exchanging optimal codons for synonymous codons in the tuf genes we quantified the selective advantage of biased codon usage in highly expressed genes to be in the range 0.2–4.2 x 10−4 per codon per generation. These data quantify for the first time the potential for selection on synonymous codon choice to drive genome-wide sequence evolution in bacteria, and in particular to optimize the sequences of highly expressed genes. This quantification may have predictive applications in the design of synthetic genes and for heterologous gene expression in biotechnology.

EuGene: maximizing synthetic gene design for heterologous expression.

UNLABELLED Numerous software applications exist to deal with synthetic gene design, granting the field of heterologous expression a significant support. However, their dispersion requires the access to different tools and online services in order to complete one single project. Analyzing codon usage, calculating codon adaptation index (CAI), aligning orthologs and optimizing genes are just a few examples. A software application, EuGene, was developed for the optimization of multiple gene synthetic design algorithms. In a seamless automatic form, EuGene calculates or retrieves genome data on codon usage (relative synonymous codon usage and CAI), codon context (CPS and codon pair bias), GC content, hidden stop codons, repetitions, deleterious sites, protein primary, secondary and tertiary structures, gene orthologs, species housekeeping genes, performs alignments and identifies genes and genomes. The main function of EuGene is analyzing and redesigning gene sequences using multi-objective optimization techniques that maximize the coding features of the resulting sequence. AVAILABILITY EuGene is freely available for non-commercial use, at http://bioinformatics.ua.pt/eugene.

Multi-omics data driven analysis establishes reference codon biases for synthetic gene design in microbial and mammalian cells

In this study, we analyzed multi-omics data and subsets thereof to establish reference codon usage biases for codon optimization in synthetic gene design. Specifically, publicly available genomic, transcriptomic, proteomic and translatomic data for microbial and mammalian expression hosts, Escherichia coli, Saccharomyces cerevisiae, Pichia pastoris and Chinese hamster ovary (CHO) cells, were compiled to derive their individual codon and codon pair frequencies. Then, host dependent and -omics specific codon biases were generated and compared by principal component analysis and hierarchical clustering. Interestingly, our results indicated the similar codon bias patterns of the highly expressed transcripts, highly abundant proteins, and efficiently translated mRNA in microbial cells, despite the general lack of correlation between mRNA and protein expression levels. However, for CHO cells, the codon bias patterns among various -omics subsets are not distinguishable, forming one cluster. Thus, we further investigated the effect of different input codon biases on codon optimized sequences using the codon context (CC) and individual codon usage (ICU) design parameters, via in silico case study on the expression of human IFNγ sequence in CHO cells. The results supported that CC is more robust design parameter than ICU for improved heterologous gene design.

A Supervisory Control Theory Approach to Control Gene Regulatory Networks

The ability to control the behavior of intracellular networks has relevant applications, ranging from an optimized synthesis of metabolites in bacteria to the cure of human diseases. To implement a controller in vivo, one may insert synthetic biomolecules into the cell being controlled. This paper develops a formal approach, based on Supervisory Control Theory (SCT), to guide the design of synthetic genes that control the dynamics of gene regulatory networks, major players in cell dynamics, so that a set of specifications is achieved. Such a systematic procedure is important to make the control of large networks by synthetic molecules feasible. In our approach, we assume that the behavior of gene networks can be abstracted by finite state machines, where each state corresponds to a set of gene expression levels and the events are associated with the activation/repression of genes. We then extend the SCT framework to address the so-called state attraction problem, for the case where the controller is allowed to: a) enable/disable events, b) force events, and c) preempt the occurrence of some (possibly all) plant events.

Exploring codon context bias for synthetic gene design of a thermostable invertase in Escherichia coli

Various isoforms of invertases from prokaryotes, fungi, and higher plants has been expressed in Escherichia coli, and codon optimisation is a widely-adopted strategy for improvement of heterologous enzyme expression. Successful synthetic gene design for recombinant protein expression can be done by matching its translational elongation rate against heterologous host organisms via codon optimization. Amongst the various design parameters considered for the gene synthesis, codon context bias has been relatively overlooked compared to individual codon usage which is commonly adopted in most of codon optimization tools. In addition, matching the rates of transcription and translation based on secondary structure may lead to enhanced protein folding. In this study, we evaluated codon context fitness as design criterion for improving the expression of thermostable invertase from Thermotoga maritima in Escherichia coli and explored the relevance of secondary structure regions for folding and expression. We designed three coding sequences by using (1) a commercial vendor optimized gene algorithm, (2) codon context for the whole gene, and (3) codon context based on the secondary structure regions. Then, the codon optimized sequences were transformed and expressed in E. coli. From the resultant enzyme activities and protein yield data, codon context fitness proved to have the highest activity as compared to the wild-type control and other criteria while secondary structure-based strategy is comparable to the control. Codon context bias was shown to be a relevant parameter for enhancing enzyme production in Escherichia coli by codon optimization. Thus, we can effectively design synthetic genes within heterologous host organisms using this criterion.

Synthetic biology towards the synthesis of custom‐made polysaccharides

Polysaccharides and their inherent structural and chemical variability provide an enormous hitherto unexploited design space for production of a range of new biobased materials. Exopolysaccharides are synthesized by numerous microorganisms (Rehm, 2010). Studies on biosynthesis provided critical information on how precursors are enzymatically diverted from primary metabolites and how they are polymerized and secreted. Polysaccharides can be composed of repeating and non-repeating building blocks, which can vary from sugars to sugar acids plus modifications including acetylation and pyruvylation. Building blocks can be single sugars (acids) or multiple sugars (acids). The vast diversity of possible polysaccharides and their corresponding differing material properties offers an almost unlimited source of biobased gel-forming materials with the potential to be made to order. In particular, viscoelastic and biological properties are often critical for the performance of these materials in medical applications such as e.g. tissue engineering, encapsulation of cells, enzymes and/or drugs for controlled and targeted delivery. Structure–function relationships i.e. linking the chemical structure of polysaccharides with materials properties will increasingly inform the in silico design of polysaccharides in regard to sugar composition, modifications and glycosidic bond configuration. Ultimately a demand for certain material properties will in silico be translated into the chemical structure of the desired polysaccharides. This will then inform the design and assembly of synthetic genes encoding enzymes and proteins for polysaccharide synthesis and secretion. Increasing knowledge about sugar metabolism and hence the possibility to engineer nucleotide sugars or sugar acids biosynthesis routes will enable to provide activated precursors that are ultimately used as building block or part of a building block. Glycosyltransferases catalyse the glycosidic linkage between the sugars/sugar acids. Rational design and engineering of glycosyltransferases will be aligned with the building block design as these transferases will specifically and sequentially incorporate individual sugars/sugar acids, which could be followed by modifications such as acetylation implementing modifying enzymes e.g. acetyltransferases. Such oligosaccharide repeating units will be synthesized on the cytosolic site of the cytoplasmic membrane linked to lipid carrier (bactoprenol, C55). The flippase (Wzx) and polymerase (Wzy) will mediate transfer of the building block across the membrane with subsequent polymerisation. The molecular mechanisms of these processes are currently not fully understood but will likely be elucidated in future for production of custom-made extracellular polysaccharides (Islam and Lam, 2013).

Computational tools and algorithms for designing customized synthetic genes.

Advances in DNA synthesis have enabled the construction of artificial genes, gene circuits, and genomes of bacterial scale. Freedom in de novo design of synthetic constructs provides significant power in studying the impact of mutations in sequence features, and verifying hypotheses on the functional information that is encoded in nucleic and amino acids. To aid this goal, a large number of software tools of variable sophistication have been implemented, enabling the design of synthetic genes for sequence optimization based on rationally defined properties. The first generation of tools dealt predominantly with singular objectives such as codon usage optimization and unique restriction site incorporation. Recent years have seen the emergence of sequence design tools that aim to evolve sequences toward combinations of objectives. The design of optimal protein-coding sequences adhering to multiple objectives is computationally hard, and most tools rely on heuristics to sample the vast sequence design space. In this review, we study some of the algorithmic issues behind gene optimization and the approaches that different tools have adopted to redesign genes and optimize desired coding features. We utilize test cases to demonstrate the efficiency of each approach, as well as identify their strengths and limitations.

GeneGenie: optimized oligomer design for directed evolution.

GeneGenie, a new online tool available at http://www.gene-genie.org, is introduced to support the design and self-assembly of synthetic genes and constructs. GeneGenie allows for the design of oligonucleotide cohorts encoding the gene sequence optimized for expression in any suitable host through an intuitive, easy-to-use web interface. The tool ensures consistent oligomer overlapping melting temperatures, minimizes the likelihood of misannealing, optimizes codon usage for expression in a selected host, allows for specification of forward and reverse cloning sequences (for downstream ligation) and also provides support for mutagenesis or directed evolution studies. Directed evolution studies are enabled through the construction of variant libraries via the optional specification of ‘variant codons’, containing mixtures of bases, at any position. For example, specifying the variant codon TNT (where N is any nucleotide) will generate an equimolar mixture of the codons TAT, TCT, TGT and TTT at that position, encoding a mixture of the amino acids Tyr, Ser, Cys and Phe. This facility is demonstrated through the use of GeneGenie to develop and synthesize a library of enhanced green fluorescent protein variants.

Codon Optimization OnLine (COOL): a web-based multi-objective optimization platform for synthetic gene design

Codon optimization has been widely used for designing synthetic genes to improve their expression in heterologous host organisms. However, most of the existing codon optimization tools consider a single design criterion and/or implement a rather rigid user interface to yield only one optimal sequence, which may not be the best solution. Hence, we have developed Codon Optimization OnLine (COOL), which is the first web tool that provides the multi-objective codon optimization functionality to aid systematic synthetic gene design. COOL supports a simple and flexible interface for customizing various codon optimization parameters such as codon adaptation index, individual codon usage and codon pairing. In addition, users can visualize and compare the optimal synthetic sequences with respect to various fitness measures. User-defined DNA sequences can also be compared against the COOL optimized sequences to show the extent by which the user's sequences can be further improved. COOL is free to academic and non-commercial users and licensed to others for a fee by the National University of Singapore. Accessible at http://bioinfo.bti.a-star.edu.sg/COOL/ CONTACT: cheld@nus.edu.sg Supplementary data are available at Bioinformatics online.