A condition-specific codon optimization approach for improved heterologous gene expression in Saccharomyces cerevisiae

All authors are with the Department of Chemical Engineering, The University of Texas at Austin, 200 E Dean Keeton St. Stop C0400, Austin, TX 78712, USA -- Hal S. Alper is with the Institute for Cellular and Molecular Biology, The University of Texas at Austin, 2500 Speedway Avenue, Austin, TX 78712, USA -- Amanda M. Lanza Current Address: Bristol-Myers Squibb, Biologics Development, 35 South Street, Hopkinton, MA 01748, USABackground: Heterologous gene expression is an important tool for synthetic biology that enables metabolic engineering and the production of non-natural biologics in a variety of host organisms. The translational efficiency of heterologous genes can often be improved by optimizing synonymous codon usage to better match the host organism. However, traditional approaches for optimization neglect to take into account many factors known to influence synonymous codon distributions. Results: Here we define an alternative approach for codon optimization that utilizes systems level information and codon context for the condition under which heterologous genes are being expressed. Furthermore, we utilize a probabilistic algorithm to generate multiple variants of a given gene. We demonstrate improved translational efficiency using this condition-specific codon optimization approach with two heterologous genes, the fluorescent protein-encoding eGFP and the catechol 1,2-dioxygenase gene CatA, expressed in S. cerevisiae. For the latter case, optimization for stationary phase production resulted in nearly 2.9-fold improvements over commercial gene optimization algorithms. Conclusions: Codon optimization is now often a standard tool for protein expression, and while a variety of tools and approaches have been developed, they do not guarantee improved performance for all hosts of applications. Here, we suggest an alternative method for condition-specific codon optimization and demonstrate its utility in Saccharomyces cerevisiae as a proof of concept. However, this technique should be applicable to any organism for which gene expression data can be generated and is thus of potential interest for a variety of applications in metabolic and cellular engineering.Chemical EngineeringInstitute for Cellular and Molecular [email protected]

Automated design of bacterial genome sequences

Background: Organisms have evolved ways of regulating transcription to better adapt to varying environments. Could the current functional genomics data and models support the possibility of engineering a genome with completely rearranged gene organization while the cell maintains its behavior under environmental challenges? How would we proceed to design a full nucleotide sequence for such genomes? Results: As a first step towards answering such questions, recent work showed that it is possible to design alternative transcriptomic models showing the same behavior under environmental variations than the wild-type model. A second step would require providing evidence that it is possible to provide a nucleotide sequence for a genome encoding such transcriptional model. We used computational design techniques to design a rewired global transcriptional regulation of Escherichia coli, yet showing a similar transcriptomic response than the wild-type. Afterwards, we “compiled” the transcriptional networks into nucleotide sequences to obtain the final genome sequence. Our computational evolution procedure ensures that we can maintain the genotype-phenotype mapping during the rewiring of the regulatory network. We found that it is theoretically possible to reorganize E. coli genome into 86% fewer regulated operons. Such refactored genomes are constituted by operons that contain sets of genes sharing around the 60% of their biological functions and, if evolved under highly variable environmental conditions, have regulatory networks, which turn out to respond more than 20% faster to multiple external perturbations. Conclusions: This work provides the first algorithm for producing a genome sequence encoding a rewired transcriptional regulation with wild-type behavior under alternative environments

Developments in the tools and methodologies of synthetic biology.

Synthetic biology is principally concerned with the rational design and engineering of biologically based parts, devices, or systems. However, biological systems are generally complex and unpredictable, and are therefore, intrinsically difficult to engineer. In order to address these fundamental challenges, synthetic biology is aiming to unify a body of knowledge from several foundational scientific fields, within the context of a set of engineering principles. This shift in perspective is enabling synthetic biologists to address complexity, such that robust biological systems can be designed, assembled, and tested as part of a biological design cycle. The design cycle takes a forward-design approach in which a biological system is specified, modeled, analyzed, assembled, and its functionality tested. At each stage of the design cycle, an expanding repertoire of tools is being developed. In this review, we highlight several of these tools in terms of their applications and benefits to the synthetic biology community

Maximizing Protein Translation Rate in the Ribosome Flow Model: the Homogeneous Case

Gene translation is the process in which intracellular macro-molecules, called ribosomes, decode genetic information in the mRNA chain into the corresponding proteins. Gene translation includes several steps. During the elongation step, ribosomes move along the mRNA in a sequential manner and link amino-acids together in the corresponding order to produce the proteins. The homogeneous ribosome flow model(HRFM) is a deterministic computational model for translation-elongation under the assumption of constant elongation rates along the mRNA chain. The HRFM is described by a set of n first-order nonlinear ordinary differential equations, where n represents the number of sites along the mRNA chain. The HRFM also includes two positive parameters: ribosomal initiation rate and the (constant) elongation rate. In this paper, we show that the steady-state translation rate in the HRFM is a concave function of its parameters. This means that the problem of determining the parameter values that maximize the translation rate is relatively simple. Our results may contribute to a better understanding of the mechanisms and evolution of translation-elongation. We demonstrate this by using the theoretical results to estimate the initiation rate in M. musculus embryonic stem cell. The underlying assumption is that evolution optimized the translation mechanism. For the infinite-dimensional HRFM, we derive a closed-form solution to the problem of determining the initiation and transition rates that maximize the protein translation rate. We show that these expressions provide good approximations for the optimal values in the n-dimensional HRFM already for relatively small values of n. These results may have applications for synthetic biology where an important problem is to re-engineer genomic systems in order to maximize the protein production rate

Quantifying evolutionary constraints on B cell affinity maturation

The antibody repertoire of each individual is continuously updated by the evolutionary process of B cell receptor mutation and selection. It has recently become possible to gain detailed information concerning this process through high-throughput sequencing. Here, we develop modern statistical molecular evolution methods for the analysis of B cell sequence data, and then apply them to a very deep short-read data set of B cell receptors. We find that the substitution process is conserved across individuals but varies significantly across gene segments. We investigate selection on B cell receptors using a novel method that side-steps the difficulties encountered by previous work in differentiating between selection and motif-driven mutation; this is done through stochastic mapping and empirical Bayes estimators that compare the evolution of in-frame and out-of-frame rearrangements. We use this new method to derive a per-residue map of selection, which provides a more nuanced view of the constraints on framework and variable regions.Comment: Previously entitled "Substitution and site-specific selection driving B cell affinity maturation is consistent across individuals

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

Gene Designer: a synthetic biology tool for constructing artificial DNA segments

BACKGROUND: Direct synthesis of genes is rapidly becoming the most efficient way to make functional genetic constructs and enables applications such as codon optimization, RNAi resistant genes and protein engineering. Here we introduce a software tool that drastically facilitates the design of synthetic genes. RESULTS: Gene Designer is a stand-alone software for fast and easy design of synthetic DNA segments. Users can easily add, edit and combine genetic elements such as promoters, open reading frames and tags through an intuitive drag-and-drop graphic interface and a hierarchical DNA/Protein object map. Using advanced optimization algorithms, open reading frames within the DNA construct can readily be codon optimized for protein expression in any host organism. Gene Designer also includes features such as a real-time sliding calculator of oligonucleotide annealing temperatures, sequencing primer generator, tools for avoidance or inclusion of restriction sites, and options to maximize or minimize sequence identity to a reference. CONCLUSION: Gene Designer is an expandable Synthetic Biology workbench suitable for molecular biologists interested in the de novo creation of genetic constructs