Metabolic engineering of muconic acid production in Saccharomyces cerevisiae.

The dicarboxylic acid muconic acid has garnered significant interest due to its potential use as a platform chemical for the production of several valuable consumer bio-plastics including nylon-6,6 and polyurethane (via an adipic acid intermediate) and polyethylene terephthalate (PET) (via a terephthalic acid intermediate). Many process advantages (including lower pH levels) support the production of this molecule in yeast. Here, we present the first heterologous production of muconic acid in the yeast Saccharomyces cerevisiae. A three-step synthetic, composite pathway comprised of the enzymes dehydroshikimate dehydratase from Podospora anserina, protocatechuic acid decarboxylase from Enterobacter cloacae, and catechol 1,2-dioxygenase from Candida albicans was imported into yeast. Further genetic modifications guided by metabolic modeling and feedback inhibition mitigation were introduced to increase precursor availability. Specifically, the knockout of ARO3 and overexpression of a feedback-resistant mutant of aro4 reduced feedback inhibition in the shikimate pathway, and the zwf1 deletion and over-expression of TKL1 increased flux of necessary precursors into the pathway. Further balancing of the heterologous enzyme levels led to a final titer of nearly 141mg/L muconic acid in a shake-flask culture, a value nearly 24-fold higher than the initial strain. Moreover, this strain has the highest titer and second highest yield of any reported shikimate and aromatic amino acid-based molecule in yeast in a simple batch condition. This work collectively demonstrates that yeast has the potential to be a platform for the bioproduction of muconic acid and suggests an area that is ripe for future metabolic engineering efforts.

Use of expression-enhancing terminators in Saccharomyces cerevisiae to increase mRNA half-life and improve gene expression control for metabolic engineering applications.

Control of gene and protein expression of both endogenous and heterologous genes is a key component of metabolic engineering. While a large amount of work has been published characterizing promoters for this purpose, less effort has been exerted to elucidate the role of terminators in yeast. In this study, we characterize over 30 terminators for use in metabolic engineering applications in Saccharomyces cerevisiae and determine mRNA half-life changes to be the major cause of the varied protein and transcript expression level. We demonstrate that the difference in transcript level can be over 6.5-fold even for high strength promoters. The influence of terminator selection is magnified when coupled with a low-expression promoter, with a maximum difference in protein expression of 11-fold between an expression-enhancing terminator and the parent plasmid terminator and over 35-fold difference when compared with a no-terminator baseline. This is the first time that terminators have been investigated in the context of multiple promoters spanning orders of magnitude in activity. Finally, we demonstrate the utility of terminator selection for metabolic engineering by using a mutant xylose isomerase gene as a proof-of-concept. Through pairing an expression-enhancing terminator with a low-expression promoter, we were able to achieve the same phenotypic result as with a promoter considerably higher in strength. Moreover, we can further boost the phenotype of the high-strength promoter by pairing it with an expression-enhancing terminator. This work highlights how terminator elements can be used to control metabolic pathways in the same way that promoters are traditionally used in yeast. Together, this work demonstrates that terminators will be an important part of heterologous gene expression and metabolic engineering for yeast in the future.

Characterization of plasmid burden and copy number in Saccharomyces cerevisiae for optimization of metabolic engineering applications.

Many metabolic engineering and genetic engineering applications in yeast rely on the use of plasmids. Despite their pervasive use and the diverse collections available, there is a fundamental lack of understanding of how commonly used DNA plasmids affect the cell's ability to grow and how the choice of plasmid components can influence plasmid load and burden. In this study, we characterized the major attributes of the 2 micron and centromeric plasmids typically used in yeast by examining the impact of choice of selection marker, promoter, origin of replication, and strain ploidy on conferred growth rates and plasmid copy number. We conclude that the 'plasmid burden', as demonstrated by a reduced growth rate, is primarily due to the choice of selection marker, especially when auxotrophic markers are utilized. The plasmid burden traditionally attributed to replication and maintenance of plasmid DNA plays only a minor role in haploid yeast yet is much more significant in diploid strains. The selection marker can also significantly change plasmid copy number. In fact, plasmid copy number can be influenced to some extent by all of the parameters tested. The information presented in this study will allow for more rational design and selection of plasmids for engineering applications.

Expanding the chemical palate of cells by combining systems biology and metabolic engineering.

The field of Metabolic Engineering has recently undergone a transformation that has led to a rapid expansion of the chemical palate of cells. Now, it is conceivable to produce nearly any organic molecule of interest using a cellular host. Significant advances have been made in the production of biofuels, biopolymers and precursors, pharmaceuticals and nutraceuticals, and commodity and specialty chemicals. Much of this rapid expansion in the field has been, in part, due to synergies and advances in the area of systems biology. Specifically, the availability of functional genomics, metabolomics and transcriptomics data has resulted in the potential to produce a wealth of new products, both natural and non-natural, in cellular factories. The sheer amount and diversity of this data however, means that uncovering and unlocking novel chemistries and insights is a non-obvious exercise. To address this issue, a number of computational tools and experimental approaches have been developed to help expedite the design process to create new cellular factories. This review will highlight many of the systems biology enabling technologies that have reduced the design cycle for engineered hosts, highlight major advances in the expanded diversity of products that can be synthesized, and conclude with future prospects in the field of metabolic engineering.

Short Synthetic Terminators for Improved Heterologous Gene Expression in Yeast.

Terminators play an important role both in completing the transcription process and impacting mRNA half-life. As such, terminators are an important synthetic component considered in applications such as heterologous gene expression and metabolic engineering. Here, we describe a panel of short (35-70 bp) synthetic terminators that can be used for modulating gene expression in yeast. The best of these synthetic terminator resulted in 3.7-fold more fluorescent protein output and 4.4-fold increase in transcript level compared to that with the commonly used CYC1 terminator. These synthetic terminators offer several advantages over native sequences, including an easily synthesized short length, minimal sequence homology to native sequences, and similar or better performance characteristics than those of commonly used longer terminators. Furthermore, the synthetic terminators are highly functional in both Saccharomyces cerevisiae and an alternative yeast, Yarrowia lipolytica, demonstrating that these synthetic designs are transferrable between diverse yeast species.

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

BACKGROUND: 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.

Design of synthetic yeast promoters via tuning of nucleosome architecture.

Model-based design of biological parts is a critical goal of synthetic biology, especially for eukaryotes. Here we demonstrate that nucleosome architecture can have a role in defining yeast promoter activity and utilize a computationally-guided approach that can enable both the redesign of endogenous promoter sequences and the de novo design of synthetic promoters. Initially, we use our approach to reprogram native promoters for increased expression and evaluate their performance in various genetic contexts. Increases in expression ranging from 1.5- to nearly 6-fold in a plasmid-based system and up to 16-fold in a genomic context were obtained. Next, we demonstrate that, in a single design cycle, it is possible to create functional, purely synthetic yeast promoters that achieve substantial expression levels (within the top sixth percentile among native yeast promoters). In doing so, this work establishes a unique DNA-level specification of promoter activity and demonstrates predictive design of synthetic parts.

Using flux balance analysis to guide microbial metabolic engineering.

Metabolic engineers modify biological systems through the use of modern molecular biology tools in order to obtain desired phenotypes. However, due to the extreme complexity and interconnectedness of metabolism in all organisms, it is often difficult to a priori predict which changes will yield the optimal results. Flux balance analysis (FBA) is a mathematical approach that uses a genomic-scale metabolic network models to afford in silico prediction and optimization of metabolic changes. In particular, a genome-scale approach can help select gene targets for knockout and overexpression. This approach can be used to help expedite the strain engineering process. Here, we give an introduction to the use of FBA and provide details for its implementation in a microbial metabolic engineering context.