EuGene: maximizing synthetic gene design for heterologous expression

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.

Strangers in a foreign land: 'Yeastizing' plant enzymes

Expressing plant metabolic pathways in microbial platforms is an efficient, cost-effective solution for producing many desired plant compounds. As eukaryotic organisms, yeasts are often the preferred platform. However, expression of plant enzymes in a yeast frequently leads to failure because the enzymes are poorly adapted to the foreign yeast cellular environment. Here, we first summarize the current engineering approaches for optimizing performance of plant enzymes in yeast. A critical limitation of these approaches is that they are labour-intensive and must be customized for each individual enzyme, which significantly hinders the establishment of plant pathways in cellular factories. In response to this challenge, we propose the development of a cost-effective computational pipeline to redesign plant enzymes for better adaptation to the yeast cellular milieu. This proposition is underpinned by compelling evidence that plant and yeast enzymes exhibit distinct sequence features that are generalizable across enzyme families. Consequently, we introduce a data-driven machine learning framework designed to extract 'yeastizing' rules from natural protein sequence variations, which can be broadly applied to all enzymes. Additionally, we discuss the potential to integrate the machine learning model into a full design-build-test cycle.

Immunogenicity and transmission-blocking potential of quiescin sulfhydryl oxidase in Plasmodium vivax

Background

Transmission-blocking vaccines (TBVs) can effectively prevent the community's spread of malaria by targeting the antigens of mosquito sexual stage parasites. At present, only a few candidate antigens have demonstrated transmission-blocking activity (TBA) potential in P. vivax. Quiescin-sulfhydryl oxidase (QSOX) is a sexual stage protein in the rodent malaria parasite Plasmodium berghei and is associated with a critical role in protein folding by introducing disulfides into unfolded reduced proteins. Here, we reported the immunogenicity and transmission-blocking potency of the PvQSOX in P. vivax.

Methods and findings

The full-length recombinant PvQSOX protein (rPvQSOX) was expressed in the Escherichia coli expression system. The anti-rPvQSOX antibodies were generated following immunization with the rPvQSOX in rabbits. A parasite integration of the pvqsox gene into the P. berghei pbqsox gene knockout genome was developed to express full-length PvQSOX protein in P. berghei (Pv-Tr-PbQSOX). In western blot, the anti-rPvQSOX antibodies recognized the native PvQSOX protein expressed in transgenic P. berghei gametocyte and ookinete. In indirect immunofluorescence assays, the fluorescence signal was detected in the sexual stages, including gametocyte, gamete, zygote, and ookinete. Anti-rPvQSOX IgGs obviously inhibited the ookinetes and oocysts development both in vivo and in vitro using transgenic parasites. Direct membrane feeding assays of anti-rPvQSOX antibodies were conducted using four field P. vivax isolates (named isolates #1-4) in Thailand. Oocyst density in mosquitoes was significantly reduced by 32.00, 85.96, 43.52, and 66.03% with rabbit anti-rPvQSOX antibodies, respectively. The anti-rPvQSOX antibodies also showed a modest reduction of infection prevalence by 15, 15, 20, and 22.22%, respectively, as compared to the control, while the effect was insignificant. The variation in the DMFA results may be unrelated to the genetic polymorphisms. Compared to the P.vivax Salvador (Sal) I strain sequences, the pvqsox in isolate #1 showed no amino acid substitution, whereas isolates #2, #3, and #4 all had the M361I substitution.

Conclusions

Our results suggest that PvQSOX could serve as a potential P. vivax TBVs candidate, which warrants further evaluation and optimization.

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.

T-FINDER: A highly sensitive, pan-HLA platform for functional T cell receptor and ligand discovery

Effective, unbiased, high-throughput methods to functionally identify both class II and class I HLA-presented T cell epitopes and their cognate T cell receptors (TCRs) are essential for and prerequisite to diagnostic and therapeutic applications, yet remain underdeveloped. Here, we present T-FINDER [T cell Functional Identification and (Neo)-antigen Discovery of Epitopes and Receptors], a system to rapidly deconvolute CD4 and CD8 TCRs and targets physiologically processed and presented by an individual's unmanipulated, complete human leukocyte antigen (HLA) haplotype. Combining a highly sensitive TCR signaling reporter with an antigen processing system to overcome previously undescribed limitations to target expression, T-FINDER both robustly identifies unknown peptide:HLA ligands from antigen libraries and rapidly screens and functionally validates the specificity of large TCR libraries against known or predicted targets. To demonstrate its capabilities, we apply the platform to multiple TCR-based applications, including diffuse midline glioma, celiac disease, and rheumatoid arthritis, providing unique biological insights and showcasing T-FINDER's potency and versatility.

In silico design of a broad-spectrum multiepitope vaccine against influenza virus

Influenza remains a global health concern due to its potential to cause pandemics as a result of rapidly mutating influenza virus strains. Existing vaccines often struggle to keep up with these rapidly mutating flu viruses. Therefore, the development of a broad-spectrum peptide vaccine that can stimulate an optimal antibody response has emerged as an innovative approach to addressing the influenza threat. In this study, an immunoinformatic approach was employed to rapidly predict immunodominant epitopes from different antigens, aiming to develop an effective multiepitope influenza vaccine (MEV). The immunodominant B-cell linear epitopes of seasonal influenza strains hemagglutinin (HA) and neuraminidase (NA) were predicted using an antibody-peptide microarray, involving a human cohort including vaccinees and infected patients. On the other hand, bioinformatics tools were used to predict immunodominant cytotoxic T-cell (CTL) and helper T-cell (HTL) epitopes. Subsequently, these epitopes were evaluated by various immunoinformatic tools. Epitopes with high antigenicity, high immunogenicity, non-allergenicity, non-toxicity, as well as exemplary conservation were then connected in series with appropriate linkers and adjuvants to construct a broad-spectrum MEV. Moreover, the structural analysis revealed that the MEV candidates exhibited good stability, and the docking results demonstrated their strong affinity to Toll-like receptors 4 (TLR4). In addition, molecular dynamics simulation confirmed the stable interaction between TLR4 and MEVs. Three injections with MEVs showed a high level of B-cell and T-cell immune responses according to the immunological simulations in silico. Furthermore, in-silico cloning was performed, and the results indicated that the MEVs could be produced in considerable quantities in Escherichia coli (E. coli). Based on these findings, it is reasonable to create a broad-spectrum MEV against different subtypes of influenza A and B viruses in silico.

Codon optimization of a gene encoding DNA polymerase from Pyrococcus furiosus and its expression in Escherichia coli

BACKGROUND

DNA polymerase is an essential component in PCR assay for DNA synthesis. Improving DNA polymerase with characteristics indispensable for a powerful assay is crucial because it can be used in wide-range applications. Derived from Pyrococcus furiosus, Pfu DNA polymerase (Pfu pol) is one of the excellent polymerases due to its high fidelity. Therefore, we aimed to develop Pfu pol from a synthetic gene with codon optimization to increase its protein yield in Escherichia coli.

RESULTS

Recombinant Pfu pol was successfully expressed and purified with a two-step purification process using nickel affinity chromatography, followed by anion exchange chromatography. Subsequently, the purified Pfu pol was confirmed by Western blot analysis, resulting in a molecular weight of approximately 90 kDa. In the final purification process, we successfully obtained a large amount of purified enzyme (26.8 mg/L). Furthermore, the purified Pfu pol showed its functionality and efficiency when tested for DNA amplification using the standard PCR.

CONCLUSIONS

Overall, a high-level expression of recombinant Pfu pol was achieved by employing our approach in the present study. In the future, our findings will be useful for studies on synthesizing recombinant DNA polymerase in E. coli expression system.

An exploratory in silico comparison of open-source codon harmonization tools

BACKGROUND

Not changing the native constitution of genes prior to their expression by a heterologous host can affect the amount of proteins synthesized as well as their folding, hampering their activity and even cell viability. Over the past decades, several strategies have been developed to optimize the translation of heterologous genes by accommodating the difference in codon usage between species. While there have been a handful of studies assessing various codon optimization strategies, to the best of our knowledge, no research has been performed towards the evaluation and comparison of codon harmonization algorithms. To highlight their importance and encourage meaningful discussion, we compared different open-source codon harmonization tools pertaining to their in silico performance, and we investigated the influence of different gene-specific factors.

RESULTS

In total, 27 genes were harmonized with four tools toward two different heterologous hosts. The difference in %MinMax values between the harmonized and the original sequences was calculated (ΔMinMax), and statistical analysis of the obtained results was carried out. It became clear that not all tools perform similarly, and the choice of tool should depend on the intended application. Almost all biological factors under investigation (GC content, RNA secondary structures and choice of heterologous host) had a significant influence on the harmonization results and thus must be taken into account. These findings were substantiated using a validation dataset consisting of 8 strategically chosen genes.

CONCLUSIONS

Due to the size of the dataset, no complex models could be developed. However, this initial study showcases significant differences between the results of various codon harmonization tools. Although more elaborate investigation is needed, it is clear that biological factors such as GC content, RNA secondary structures and heterologous hosts must be taken into account when selecting the codon harmonization tool.

Excel-SBOL Converter: Creating SBOL from Excel Templates and Vice Versa

Standards support synthetic biology research by enabling the exchange of component information. However, using formal representations, such as the Synthetic Biology Open Language (SBOL), typically requires either a thorough understanding of these standards or a suite of tools developed in concurrence with the ontologies. Since these tools may be a barrier for use by many practitioners, the Excel-SBOL Converter was developed to facilitate the use of SBOL and integration into existing workflows. The converter consists of two Python libraries: one that converts Excel templates to SBOL and another that converts SBOL to an Excel workbook. Both libraries can be used either directly or via a SynBioHub plugin.

A BioBricks Metabolic Engineering Platform for the Biosynthesis of Anthracyclinones in Streptomyces coelicolor

Actinomycetes produce a variety of clinically indispensable molecules, such as antineoplastic anthracyclines. However, the actinomycetes are hindered in their further development as genetically engineered hosts for the synthesis of new anthracycline analogues due to their slow growth kinetics associated with their mycelial life cycle and the lack of a comprehensive genetic toolbox for combinatorial biosynthesis. In this report, we tackled both issues via the development of the BIOPOLYMER (BIOBricks POLYketide Metabolic EngineeRing) toolbox: a comprehensive synthetic biology toolbox consisting of engineered strains, promoters, vectors, and biosynthetic genes for the synthesis of anthracyclinones. An improved derivative of the production host Streptomyces coelicolor M1152 was created by deleting the matAB gene cluster that specifies extracellular poly-β-1,6-N-acetylglucosamine (PNAG). This resulted in a loss of mycelial aggregation, with improved biomass accumulation and anthracyclinone production. We then leveraged BIOPOLYMER to engineer four distinct anthracyclinone pathways, identifying optimal combinations of promoters, genes, and vectors to produce aklavinone, 9-epi-aklavinone, auramycinone, and nogalamycinone at titers between 15-20 mg/L. Optimization of nogalamycinone production strains resulted in titers of 103 mg/L. We structurally characterized six anthracyclinone products from fermentations, including new compounds 9,10-seco-7-deoxy-nogalamycinone and 4-O-β-d-glucosyl-nogalamycinone. Lastly, we tested the antiproliferative activity of the anthracyclinones in a mammalian cancer cell viability assay, in which nogalamycinone, auramycinone, and aklavinone exhibited moderate cytotoxicity against several cancer cell lines. We envision that BIOPOLYMER will serve as a foundational platform technology for the synthesis of designer anthracycline analogues.

Validation of Promoters and Codon Optimization on CRISPR/Cas9-Engineered Jurkat Cells Stably Expressing αRep4E3 for Interfering with HIV-1 Replication

Persistent and efficient therapeutic protein expression in the specific target cell is a significant concern in gene therapy. The controllable integration site, suitable promoter, and proper codon usage influence the effectiveness of the therapeutic outcome. Previously, we developed a non-immunoglobulin scaffold, alpha repeat protein (αRep4E3), as an HIV-1 RNA packaging interference system in SupT1 cells using the lentiviral gene transfer. Although the success of anti-HIV-1 activity was evidenced, the integration site is uncontrollable and may not be practical for clinical translation. In this study, we use the CRISPR/Cas9 gene editing technology to precisely knock-in αRep4E3 genes into the adeno-associated virus integration site 1 (AAVS1) safe harbor locus of the target cells. We compare the αRep4E3 expression under the regulation of three different promoters, including cytomegalovirus (CMV), human elongation factor-1 alpha (EF1α), and ubiquitin C (UbC) promoters with and without codon optimization in HEK293T cells. The results demonstrated that the EF1α promoter with codon-optimized αRep4E3mCherry showed higher protein expression than other promoters with non-optimized codons. We then performed a proof-of-concept study by knocking in the αRep4E3mCherry gene at the AAVS1 locus of the Jurkat cells. The results showed that the αRep4E3mCherry-expressing Jurkat cells exhibited anti-HIV-1 activities against HIV-1NL4-3 strain as evidenced by decreased capsid (p24) protein levels and viral genome copies as compared to the untransfected Jurkat control cells. Altogether, our study demonstrates that the αRep4E3 could interfere with the viral RNA packaging and suggests that the αRep4E3 scaffold protein could be a promising anti-viral molecule that offers a functional cure for people living with HIV-1.

Expression of Codon-Optimized Gene Encoding Murine Moloney Leukemia Virus Reverse Transcriptase in Escherichia coli

Moloney murine leukemia virus reverse transcriptase (MMLV-RT) is the most frequently used enzyme in molecular biology for cDNA synthesis. To date, reverse transcription coupled with Polymerase Chain Reaction, known as RT-PCR, has been popular as an excellent approach for the detection of SARS-CoV-2 during the COVID-19 pandemic. In this study, we aimed to improve the enzymatic production and performance of MMLV-RT by optimizing both codon and culture conditions in E. coli expression system. By applying the optimized codon and culture conditions, the enzyme was successfully overexpressed and increased at high level based on the result of SDS-PAGE and Western blotting. The total amount of MMLV-RT has improved 85-fold from 0.002 g L^-1 to 0.175 g L^-1 of culture. One-step purification by nickel affinity chromatography has been performed to generate the purified enzyme for further analysis of qualitative and quantitative RT activity. Overall, our investigation provides useful strategies to enhance the recombinant enzyme of MMLV-RT in both production and performance. More importantly, the enzyme has shown promising activity to be used for RT-PCR assay.

Design of typical genes for heterologous gene expression

Heterologous protein expression is an important method for analysing cellular functions of proteins, in genetic circuit engineering and in overexpressing proteins for biopharmaceutical applications and structural biology research. The degeneracy of the genetic code, which enables a single protein to be encoded by a multitude of synonymous gene sequences, plays an important role in regulating protein expression, but substantial uncertainty exists concerning the details of this phenomenon. Here we analyse the influence of a profiled codon usage adaptation approach on protein expression levels in the eukaryotic model organism Saccharomyces cerevisiae. We selected green fluorescent protein (GFP) and human α-synuclein (αSyn) as representatives for stable and intrinsically disordered proteins and representing a benchmark and a challenging test case. A new approach was implemented to design typical genes resembling the codon usage of any subset of endogenous genes. Using this approach, synthetic genes for GFP and αSyn were generated, heterologously expressed and evaluated in yeast. We demonstrate that GFP is expressed at high levels, and that the toxic αSyn can be adapted to endogenous, low-level expression. The new software is publicly available as a web-application for performing host-specific protein adaptations to a set of the most commonly used model organisms ( https://odysseus.motorprotein.de ).

Optimal Design of Synthetic DNA Sequences Without Unwanted Binding Sites

Synthesizing DNA molecules by design has become an essential tool in molecular biology and is expected to become ubiquitous in the coming decade. Successful design of a synthetic DNA molecule often requires satisfying multiple objectives, some of which may conflict with others. One particularly important objective is the elimination of unwanted protein binding sites, which may interfere with the desired function of the synthesized molecule. While most design tools offer this fundamental capability, they do not follow a systematic approach that guarantees elimination of all unwanted sites whenever a feasible solution exists. Furthermore, the algorithms these tools use (when published) are often quite naive and inefficient. We present a formal description of the binding site elimination problem and suggest several efficient algorithms that eliminate unwanted patterns with minimum interference to the desired function of the synthesized sequence. These algorithms are simple, efficient, and flexible and, therefore, can be easily incorporated in all existing DNA design tools, enhancing their design capabilities.

Engineering genetic circuits: advancements in genetic design automation tools and standards for synthetic biology

Synthetic biology (SynBio) is a field at the intersection of biology and engineering. Inspired by engineering principles, researchers use defined parts to build functionally defined biological circuits. Genetic design automation (GDA) allows scientists to design, model, and analyze their genetic circuits in silico before building them in the lab, saving time, and resources in the process. Establishing SynBio's future is dependent on GDA, since the computational approach opens the field to a broad, interdisciplinary community. However, challenges with part libraries, standards, and software tools are currently stalling progress in the field. This review first covers recent advancements in GDA, followed by an assessment of the challenges ahead, and a proposed automated genetic design workflow for the future.

Exploiting the Gal4/UAS System as Plant Orthogonal Molecular Toolbox to Control Reporter Expression in Arabidopsis Protoplasts

The ability of protein domains to fold independently from the rest of the polypeptide is the principle governing the generation of fusion proteins with customized functions. A clear example is the split transcription factor system based on the yeast GAL4 protein and its cognate UAS enhancer. The rare occurrence of the UAS element in the transcriptionally sensitive regions of the Arabidopsis genome makes this transcription factor an ideal orthogonal platform to control reporter induction. Moreover, heterodimeric transcriptional complexes can be generated by exploiting posttranslational modifications hampering or promoting the interaction between GAL4-fused transcriptional partners, whenever this leads to the reconstitution of a fully functional GAL4 factor.The assembly of multiple engineered proteins into a synthetic transcriptional complex requires preliminary testing, before its components can be stably introduced into the plant genome. Mesophyll protoplast transformation represents a fast and reliable technique to test and optimize synthetic regulatory modules. Remarkable properties are the possibility to transform different combinations of plasmids (co-transformation) and the physiological resemblance of these isolated cells with the original tissue.Here we describe an extensive protocol to produce and exploit Arabidopsis mesophyll protoplasts to investigate the transcriptional output of GAL4/UAS-based complexes that are sensitive to posttranslational protein modifications.

Tailoring Codon Usage to the Underlying Biology for Protein Expression Optimization

For heterologous gene expression, codon optimization is required to enhance the quality and quantity of the protein product. Recently, we introduced the software tool OCTOPOS. This sequence optimizer combines a detailed mechanistic mathematical modeling of in vivo protein synthesis with a state-of-the-art machine learning algorithm to find the sequence that best serves a user's needs. Here, we briefly describe the algorithm and its implementation as well as its application in practice using OCTOPOS.

Toward Full-Stack In Silico Synthetic Biology: Integrating Model Specification, Simulation, Verification, and Biological Compilation

We present the Infobiotics Workbench (IBW), a user-friendly, scalable, and integrated computational environment for the computer-aided design of synthetic biological systems. It supports an iterative workflow that begins with specification of the desired synthetic system, followed by simulation and verification of the system in high-performance environments and ending with the eventual compilation of the system specification into suitable genetic constructs. IBW integrates modeling, simulation, verification, and biocompilation features into a single software suite. This integration is achieved through a new domain-specific biological programming language, the Infobiotics Language (IBL), which tightly combines these different aspects of in silico synthetic biology into a full-stack integrated development environment. Unlike existing synthetic biology modeling or specification languages, IBL uniquely blends modeling, verification, and biocompilation statements into a single file. This allows biologists to incorporate design constraints within the specification file rather than using decoupled and independent formalisms for different in silico analyses. This novel approach offers seamless interoperability across different tools as well as compatibility with SBOL and SBML frameworks and removes the burden of doing manual translations for standalone applications. We demonstrate the features, usability, and effectiveness of IBW and IBL using well-established synthetic biological circuits.

Learning the Regulatory Code of Gene Expression

Data-driven machine learning is the method of choice for predicting molecular phenotypes from nucleotide sequence, modeling gene expression events including protein-DNA binding, chromatin states as well as mRNA and protein levels. Deep neural networks automatically learn informative sequence representations and interpreting them enables us to improve our understanding of the regulatory code governing gene expression. Here, we review the latest developments that apply shallow or deep learning to quantify molecular phenotypes and decode the cis-regulatory grammar from prokaryotic and eukaryotic sequencing data. Our approach is to build from the ground up, first focusing on the initiating protein-DNA interactions, then specific coding and non-coding regions, and finally on advances that combine multiple parts of the gene and mRNA regulatory structures, achieving unprecedented performance. We thus provide a quantitative view of gene expression regulation from nucleotide sequence, concluding with an information-centric overview of the central dogma of molecular biology.

Expression of novel acidic lipase from Micrococcus luteus in Pichia pastoris and its application in transesterification

Background

Lipases are promising biocatalysts for industrial applications and attract attention to be explored. A novel acidic lipase has been isolated from the lipolytic bacteria Micrococcus luteus EMP48-D (LipEMP48-D) screened from tempeh. The lipase gene had previously been overexpressed in Escherichia coli BL21, but the expression level obtained was relatively low. Here, to improve the expression level, the lipase gene was cloned to Pichia pastoris. We eliminated the native signal sequence of M. luteus and replaced it with α-mating factor (α-MF) signal sequence. We also optimized and synthesized the lipase gene based on codon preference in P. pastoris.

Results

LipEMP48-D lipase was expressed as an extracellular protein. Codon optimization has been conducted for 20 codons, with the codon adaption index reaching 0.995. The highest extracellular lipase activity obtained reached 145.4 ± 4.8 U/mg under AOX1 promoter in P. pastoris KM71 strain, which was 9.7-fold higher than the previous activity in E. coli. LipEMP48-D showed the highest specific activity at pH 5.0 and stable within the pH range 3.0–5.0 at 40 °C. LipEMP48-D also has the capability of hydrolyzing various long-chain triglycerides, particularly olive oil (100%) followed by sunflower oil (88.5%). LipEMP48-D exhibited high tolerance for various polar organic solvents with low log P, such as isopropanol (115.7%) and butanol (114.6%). The metal ions (Na⁺, K⁺, Ca²⁺, Mg²⁺, Mn⁺) decreased enzyme activity up to 43.1%, while Fe²⁺ increased relative activity of enzymes up to 200%. The conversion of free fatty acid (FFA) into fatty acid methyl ester (FAME) was low around 2.95%.

Conclusions

This study was the first to report overexpression of Micrococcus lipase in yeast. The extracellular expression of this acidic lipase could be potential for biocatalyst in industrial fields, especially organic synthesis, food industry, and production of biodiesel.

Genetic and metabolic engineering challenges of C1-gas fermenting acetogenic chassis organisms

Unabated mining and utilisation of petroleum and petroleum resources and their conversion to essential fuels and chemicals have drastic environmental consequences, contributing to global warming and climate change. In addition, fossil fuels are finite resources, with a fast-approaching shortage. Accordingly, research efforts are increasingly focusing on developing sustainable alternatives for chemicals and fuels production. In this context, bioprocesses, relying on microorganisms, have gained particular interest. For example, acetogens use the Wood-Ljungdahl pathway to grow on single carbon C1-gases (CO2 and CO) as their sole carbon source and produce valuable products such as acetate or ethanol. These autotrophs can, therefore, be exploited for large-scale fermentation processes to produce industrially relevant chemicals from abundant greenhouse gases. In addition, genetic tools have recently been developed to improve these chassis organisms through synthetic biology approaches. This review will focus on the challenges of genetically and metabolically modifying acetogens. It will first discuss the physical and biochemical obstacles complicating successful DNA transfer in these organisms. Current genetic tools developed for several acetogens, crucial for strain engineering to consolidate and expand their catalogue of products, will then be described. Recent tool applications for metabolic engineering purposes to allow redirection of metabolic fluxes or production of non-native compounds will lastly be covered.

Codon optimization with deep learning to enhance protein expression

Heterologous expression is the main approach for recombinant protein production ingenetic synthesis, for which codon optimization is necessary. The existing optimization methods are based on biological indexes. In this paper, we propose a novel codon optimization method based on deep learning. First, we introduce the concept of codon boxes, via which DNA sequences can be recoded into codon box sequences while ignoring the order of bases. Then, the problem of codon optimization can be converted to sequence annotation of corresponding amino acids with codon boxes. The codon optimization models for Escherichia Coli were trained by the Bidirectional Long-Short-Term Memory Conditional Random Field. Theoretically, deep learning is a good method to obtain the distribution characteristics of DNA. In addition to the comparison of the codon adaptation index, protein expression experiments for plasmodium falciparum candidate vaccine and polymerase acidic protein were implemented for comparison with the original sequences and the optimized sequences from Genewiz and ThermoFisher. The results show that our method for enhancing protein expression is efficient and competitive.

COSMO: A dynamic programming algorithm for multicriteria codon optimization

Codon optimization in protein-coding sequences (CDSs) is a widely used technique to promote the heterologous expression of target genes. In codon optimization, a combinatorial space of nucleotide sequences that code a given amino acid sequence and take into account user-prescribed forbidden sequence motifs is explored to optimize multiple criteria. Although evolutionary algorithms have been used to tackle such complex codon optimization problems, evolutionary codon optimization tools do not provide guarantees to find the optimal solutions for these multicriteria codon optimization problems.

We have developed a novel multicriteria dynamic programming algorithm, COSMO. By using this algorithm, we can obtain all Pareto-optimal solutions for the multiple features of CDS, which include codon usage, codon context, and the number of hidden stop codons. User-prescribed forbidden sequence motifs are rigorously excluded from the Pareto-optimal solutions. To accelerate CDS design by COSMO, we introduced constraints that reduce the number of Pareto-optimal solutions to be processed in a branch-and-bound manner. We benchmarked COSMO for run-time and the number of generated solutions by adapting selected human genes to yeast codon usage frequencies, and found that the constraints effectively reduce the run-time. In addition to the benchmarking of COSMO, a multi-objective genetic algorithm (MOGA) for CDS design was also benchmarked for the same two aspects and their performances were compared. In this comparison, (i) MOGA identified significantly fewer Pareto-optimal solutions than COSMO, and (ii) the MOGA solutions did not achieve the same mean hypervolume values as those provided by COSMO. These results suggest that generating the whole set of the Pareto-optimal solutions of the codon optimization problems is a difficult task for MOGA.

ChimeraUGEM: unsupervised gene expression modeling in any given organism

MOTIVATION

Regulation of the amount of protein that is synthesized from genes has proved to be a serious challenge in terms of analysis and prediction, and in terms of engineering and optimization, due to the large diversity in expression machinery across species.

RESULTS

To address this challenge, we developed a methodology and a software tool (ChimeraUGEM) for predicting gene expression as well as adapting the coding sequence of a target gene to any host organism. We demonstrate these methods by predicting protein levels in seven organisms, in seven human tissues, and by increasing in vivo the expression of a synthetic gene up to 26-fold in the single-cell green alga Chlamydomonas reinhardtii. The underlying model is designed to capture sequence patterns and regulatory signals with minimal prior knowledge on the host organism and can be applied to a multitude of species and applications.

AVAILABILITY AND IMPLEMENTATION

Source code (MATLAB, C) and binaries are freely available for download for non-commercial use at http://www.cs.tau.ac.il/~tamirtul/ChimeraUGEM/, and supported on macOS, Linux and Windows.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Improving heterologous protein expression in Synechocystis sp. PCC 6803 for alpha-bisabolene production

Cyanobacterial biofuels have the potential to reduce the cost and climate impacts of biofuel production because primary carbon fixation and conversion to fuel are completed together in the cultivation of the cyanobacteria. Cyanobacterial biofuels, therefore, do not rely on costly organic carbon feedstocks that heterotrophs require, which reduces competition for agricultural resources such as arable land and freshwater. However, the published product titer achieved for most molecules of interest using cyanobacteria lag behind what has been achieved using yeast and Escherichia coli (E. coli) cultures. In Synechocystis sp. PCC 6803 (S. 6803), we attempted to increase the product titer of the sesquiterpene, bisabolene, which may be converted to bisabolane, a possible diesel replacement. We tested 19 strains of genetically modified S. 6803 with five different codon usage sequences of the bisabolene synthase from the grand fir tree (Abies grandis). At least three ribosome binding sites (most designed using the RBS Calculator) were tested for each codon usage sequence. We also tested strains with and without the farnesyl pyrophosphate synthase gene from E. coli. Bisabolene titers after five days of growth in continuous light ranged from un-detected to 7.8 ​mg/L. Bisabolene synthase abundance was measured and found to be well correlated with titer. Select strains were also tested in 12:12 light:dark cycles, where similar titers were reached after the same amount of light exposure time. One engineered strain was also tested in photobioreactors exposed to a simulated outdoor light pattern with maximum light intensity of 1600 ​μmol photons m-2 s-1. Here, the bisabolene titer reached 22.2 ​mg/L after 36 days of growth. Dramatic improvements in our ability to control gene expression in cyanobacteria such as S. 6803, and the co-utilization of additional metabolic engineering methods, are needed in order for these titers to improve to the levels reported for engineered E. coli.

Codon optimization: a mathematical programing approach

Motivation

Synthesizing proteins in heterologous hosts is an important tool in biotechnology. However, the genetic code is degenerate and the codon usage is biased in many organisms. Synonymous codon changes that are customized for each host organism may have a significant effect on the level of protein expression. This effect can be measured by using metrics, such as codon adaptation index, codon pair bias, relative codon bias and relative codon pair bias. Codon optimization is designing codons that improve one or more of these objectives. Currently available algorithms and software solutions either rely on heuristics without providing optimality guarantees or are very rigid in modeling different objective functions and restrictions.

Results

We develop an effective mixed integer linear programing (MILP) formulation, which considers multiple objectives. Our numerical study shows that this formulation can be effectively used to generate (Pareto) optimal codon designs even for very long amino acid sequences using a standard commercial solver. We also show that one can obtain designs in the efficient frontier in reasonable solution times and incorporate other complex objectives, such as mRNA secondary structures in codon design using MILP formulations.

Availability and implementation

http://alpersen.bilkent.edu.tr/codonoptimization/CodonOptimization.zip.

Computational Tools for Discovering and Engineering Natural Product Biosynthetic Pathways

Natural products (NPs), also known as secondary metabolites, are produced in bacteria, fungi, and plants. NPs represent a rich source of antibacterial, antifungal, and anticancer agents. Recent advances in DNA sequencing technologies and bioinformatics unveiled nature's great potential for synthesizing numerous NPs that may confer unprecedented structural and biological features. However, discovering novel bioactive NPs by genome mining remains a challenge. Moreover, even with interesting bioactivity, the low productivity of many NPs significantly limits their practical applications. Here we discuss the progress in developing bioinformatics tools for efficient discovery of bioactive NPs. In addition, we highlight computational methods for optimizing the productivity of NPs of pharmaceutical importance.

Optimizing the dynamics of protein expression

Heterologously expressed genes require adaptation to the host organism to ensure adequate levels of protein synthesis, which is typically approached by replacing codons by the target organism’s preferred codons. In view of frequently encountered suboptimal outcomes we introduce the codon-specific elongation model (COSEM) as an alternative concept. COSEM simulates ribosome dynamics during mRNA translation and informs about protein synthesis rates per mRNA in an organism- and context-dependent way. Protein synthesis rates from COSEM are integrated with further relevant covariates such as translation accuracy into a protein expression score that we use for codon optimization. The scoring algorithm further enables fine-tuning of protein expression including deoptimization and is implemented in the software OCTOPOS. The protein expression score produces competitive predictions on proteomic data from prokaryotic, eukaryotic, and human expression systems. In addition, we optimized and tested heterologous expression of manA and ova genes in Salmonella enterica serovar Typhimurium. Superiority over standard methodology was demonstrated by a threefold increase in protein yield compared to wildtype and commercially optimized sequences.

The structure of the TsaB/TsaD/TsaE complex reveals an unexpected mechanism for the bacterial t6A tRNA-modification

The universal N6-threonylcarbamoyladenosine (t6A) modification at position A37 of ANN-decoding tRNAs is essential for translational fidelity. In bacteria the TsaC enzyme first synthesizes an l-threonylcarbamoyladenylate (TC-AMP) intermediate. In cooperation with TsaB and TsaE, TsaD then transfers the l-threonylcarbamoyl-moiety from TC-AMP onto tRNA. We determined the crystal structure of the TsaB-TsaE-TsaD (TsaBDE) complex of Thermotoga maritima in presence of a non-hydrolysable AMPCPP. TsaE is positioned at the entrance of the active site pocket of TsaD, contacting both the TsaB and TsaD subunits and prohibiting simultaneous tRNA binding. AMPCPP occupies the ATP binding site of TsaE and is sandwiched between TsaE and TsaD. Unexpectedly, the binding of TsaE partially denatures the active site of TsaD causing loss of its essential metal binding sites. TsaE interferes in a pre- or post-catalytic step and its binding to TsaBD is regulated by ATP hydrolysis. This novel binding mode and activation mechanism of TsaE offers good opportunities for antimicrobial drug development.

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.

Local genic base composition impacts protein production and cellular fitness

The maintenance of a G + C content that is higher than the mutational input to a genome provides support for the view that selection serves to increase G + C contents in bacteria. Recent experimental evidence from Escherichia coli demonstrated that selection for increasing G + C content operates at the level of translation, but the precise mechanism by which this occurs is unknown. To determine the substrate of selection, we asked whether selection on G + C content acts across all sites within a gene or is confined to particular genic regions or nucleotide positions. We systematically altered the G + C contents of the GFP gene and assayed its effects on the fitness of strains harboring each variant. Fitness differences were attributable to the base compositional variation in the terminal portion of the gene, suggesting a connection to the folding of a specific protein feature. Variants containing sequence features that are thought to result in rapid translation, such as low G + C content and high levels of codon adaptation, displayed highly reduced growth rates. Taken together, our results show that purifying selection acting against A and T mutations most likely results from their tendency to increase the rate of translation, which can perturb the dynamics of protein folding.

Optimizing assembly and production of native bispecific antibodies by codon de-optimization

When production of bispecific antibodies requires the co-expression and assembly of three or four polypeptide chains, low expression of one chain can significantly limit assembly and yield. κλ bodies, fully human bispecific antibodies with native IgG structure, are composed of a common heavy chain and two different light chains, one kappa and one lambda. No engineering is applied to force pairing of the chains, thus both monospecific and bispecific antibodies are secreted in the supernatant. In this context, stoichiometric expression of the two light chains allows for maximal assembly of the bispecific antibody. In this study, we selected a κλ body with suboptimal characteristics due to low kappa chain expression. Codon optimization to increase expression of the kappa chain did not improve bispecific yield. Surprisingly, progressive introduction of non-optimal codons into the sequence of the lambda chain resulted in lowering its expression for an optimal tuning of the relative distribution of monospecific and bispecific antibodies. This codon de-optimization led to doubling of the κλ body yield. These results indicate that assembly of different proteins into a recombinant complex is an interconnected process and that reducing the expression of one polypeptide can actually increase the overall yield.

Novel methods to optimize gene and statistic test for evaluation - an application for Escherichia coli

Background

Since the recombinant protein was discovered, it has become more popular in many aspects of life science. The value of global pharmaceutical market was $87 billion in 2008 and the sales for industrial enzyme exceeded $4 billion in 2012. This is strong evidence showing the great potential of recombinant protein. However, native genes introduced into a host can cause incompatibility of codon usage bias, GC content, repeat region, Shine-Dalgarno sequence with host’s expression system, so the yields can fall down significantly. Hence, we propose novel methods for gene optimization based on neural network, Bayesian theory, and Euclidian distance.

Result

The correlation coefficients of our neural network are 0.86, 0.73, and 0.90 in training, validation, and testing process. In addition, genes optimized by our methods seem to associate with highly expressed genes and give reasonable codon adaptation index values. Furthermore, genes optimized by the proposed methods are highly matched with the previous experimental data.

Conclusion

The proposed methods have high potential for gene optimization and further researches in gene expression. We built a demonstrative program using Matlab R2014a under Mac OS X. The program was published in both standalone executable program and Matlab function files. The developed program can be accessed from http://www.math.hcmus.edu.vn/~ptbao/paper_soft/GeneOptProg/.

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.

Engineering Translation in Mammalian Cell Factories to Increase Protein Yield: The Unexpected Use of Long Non-Coding SINEUP RNAs

Mammalian cells are an indispensable tool for the production of recombinant proteins in contexts where function depends on post-translational modifications. Among them, Chinese Hamster Ovary (CHO) cells are the primary factories for the production of therapeutic proteins, including monoclonal antibodies (MAbs). To improve expression and stability, several methodologies have been adopted, including methods based on media formulation, selective pressure and cell- or vector engineering. This review presents current approaches aimed at improving mammalian cell factories that are based on the enhancement of translation. Among well-established techniques (codon optimization and improvement of mRNA secondary structure), we describe SINEUPs, a family of antisense long non-coding RNAs that are able to increase translation of partially overlapping protein-coding mRNAs. By exploiting their modular structure, SINEUP molecules can be designed to target virtually any mRNA of interest, and thus to increase the production of secreted proteins. Thus, synthetic SINEUPs represent a new versatile tool to improve the production of secreted proteins in biomanufacturing processes.

CDSfold: an algorithm for designing a protein-coding sequence with the most stable secondary structure

Motivation: An important problem in synthetic biology is to design a nucleotide sequence of an mRNA that confers a desirable expression level of a target protein. The secondary structure of protein-coding sequences (CDSs) is one potential factor that could have both positive and negative effects on protein production. To elucidate the role of secondary structure in CDSs, algorithms for manipulating secondary structure should be developed.

Results: We developed an algorithm for designing a CDS with the most stable secondary structure among all possible ones translated into the same protein, and implemented it as the program CDSfold. The algorithm runs the Zuker algorithm under the constraint of a given amino acid sequence. The time and space complexity is O(L3) and O(L2), respectively, where L is the length of the CDS to be designed. Although our algorithm is slower than the original Zuker algorithm, it could design a relatively long (2.7-kb) CDS in approximately 1 h.

Availability and implementation: The CDSfold program is freely available for non-commercial users as stand-alone and web-based software from http://cdsfold.trahed.jp/cdsfold/.

Contacts: terai-goro@aist.go.jp or asai@k.u-tokyo.ac.jp

Supplementary information: Supplementary data are available at Bioinformatics online.

Decoding mechanisms by which silent codon changes influence protein biogenesis and function

SCOPE

Synonymous codon usage has been a focus of investigation since the discovery of the genetic code and its redundancy. The occurrences of synonymous codons vary between species and within genes of the same genome, known as codon usage bias. Today, bioinformatics and experimental data allow us to compose a global view of the mechanisms by which the redundancy of the genetic code contributes to the complexity of biological systems from affecting survival in prokaryotes, to fine tuning the structure and function of proteins in higher eukaryotes. Studies analyzing the consequences of synonymous codon changes in different organisms have revealed that they impact nucleic acid stability, protein levels, structure and function without altering amino acid sequence. As such, synonymous mutations inevitably contribute to the pathogenesis of complex human diseases. Yet, fundamental questions remain unresolved regarding the impact of silent mutations in human disorders. In the present review we describe developments in this area concentrating on mechanisms by which synonymous mutations may affect protein function and human health.

PURPOSE

This synopsis illustrates the significance of synonymous mutations in disease pathogenesis. We review the different steps of gene expression affected by silent mutations, and assess the benefits and possible harmful effects of codon optimization applied in the development of therapeutic biologics.

PHYSIOLOGICAL AND MEDICAL RELEVANCE

Understanding mechanisms by which synonymous mutations contribute to complex diseases such as cancer, neurodegeneration and genetic disorders, including the limitations of codon-optimized biologics, provides insight concerning interpretation of silent variants and future molecular therapies.

Recombinant Pvs48/45 antigen expressed in E. coli generates antibodies that block malaria transmission in Anopheles albimanus mosquitoes

Transmission of malaria parasites from humans to Anopheles mosquitoes can be inhibited by specific antibodies elicited during malaria infection, which target surface Plasmodium gametocyte/gamete proteins. Some of these proteins may have potential for vaccine development. Pvs48/45 is a P. vivax gametocyte surface antigen orthologous to Pfs48/45, which may play a role during parasite fertilization and thus has potential for transmission blocking (TB) activity. Here we describe the expression of a recombinant Pvs48/45 protein expressed in Escherichia coli as a ∼60kDa construct which we tested for antigenicity using human sera and for its immunogenicity and transmission blocking activity of specific anti-mouse and anti-monkey Pvs48/45 antibodies. The protein reacted with sera of individuals from malaria-endemic areas and in addition induced specific IgG antibody responses in BALB/c mice and Aotus l. griseimembra monkeys. Sera from both immunized animal species recognized native P. vivax protein in Western blot (WB) and immunofluorescence assays. Moreover, sera from immunized mice and monkeys produced significant inhibition of parasite transmission to An. Albimanus mosquitoes as shown by membrane feeding assays. Results indicate the presence of reactive epitopes in the Pvs48/45 recombinant product that induce antibodies with TB activity. Further testing of this protein is ongoing to determine its vaccine potential.

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.

A critical analysis of codon optimization in human therapeutics

Codon optimization describes gene engineering approaches that use synonymous codon changes to increase protein production. Applications for codon optimization include recombinant protein drugs and nucleic acid therapies, including gene therapy, mRNA therapy, and DNA/RNA vaccines. However, recent reports indicate that codon optimization can affect protein conformation and function, increase immunogenicity, and reduce efficacy. We critically review this subject, identifying additional potential hazards including some unique to nucleic acid therapies. This analysis highlights the evolved complexity of codon usage and challenges the scientific bases for codon optimization. Consequently, codon optimization may not provide the optimal strategy for increasing protein production and may decrease the safety and efficacy of biotech therapeutics. We suggest that the use of this approach is reconsidered, particularly for in vivo applications.

Protein secretion biotechnology in Gram-positive bacteria with special emphasis on Streptomyces lividans

Proteins secreted by Gram-positive bacteria are released into the culture medium with the obvious benefit that they usually retain their native conformation. This property makes these host cells potentially interesting for the production of recombinant proteins, as one can take full profit of established protocols for the purification of active proteins. Several state-of-the-art strategies to increase the yield of the secreted proteins will be discussed, using Streptomyces lividans as an example and compared with approaches used in some other host cells. It will be shown that approaches such as increasing expression and translation levels, choice of secretion pathway and modulation of proteins thereof, avoiding stress responses by changing expression levels of specific (stress) proteins, can be helpful to boost production yield. In addition, the potential of multi-omics approaches as a tool to understand the genetic background and metabolic fluxes in the host cell and to seek for new targets for strain and protein secretion improvement is discussed. It will be shown that S. lividans, along with other Gram-positive host cells, certainly plays a role as a production host for recombinant proteins in an economically viable way. This article is part of a Special Issue entitled: Protein trafficking and secretion in bacteria. Guest Editors: Anastassios Economou and Ross Dalbey.