Bacterial genome reduction using the progressive clustering of deletions via yeast sexual cycling.

The availability of genetically tractable organisms with simple genomes is critical for the rapid, systems-level understanding of basic biological processes. Mycoplasma bacteria, with the smallest known genomes among free-living cellular organisms, are ideal models for this purpose, but the natural versions of these cells have genome complexities still too great to offer a comprehensive view of a fundamental life form. Here we describe an efficient method for reducing genomes from these organisms by identifying individually deletable regions using transposon mutagenesis and progressively clustering deleted genomic segments using meiotic recombination between the bacterial genomes harbored in yeast. Mycoplasmal genomes subjected to this process and transplanted into recipient cells yielded two mycoplasma strains. The first simultaneously lacked eight singly deletable regions of the genome, representing a total of 91 genes and ∼ 10% of the original genome. The second strain lacked seven of the eight regions, representing 84 genes. Growth assay data revealed an absence of genetic interactions among the 91 genes under tested conditions. Despite predicted effects of the deletions on sugar metabolism and the proteome, growth rates were unaffected by the gene deletions in the seven-deletion strain. These results support the feasibility of using single-gene disruption data to design and construct viable genomes lacking multiple genes, paving the way toward genome minimization. The progressive clustering method is expected to be effective for the reorganization of any mega-sized DNA molecules cloned in yeast, facilitating the construction of designer genomes in microbes as well as genomic fragments for genetic engineering of higher eukaryotes.

Dynamic model-based analysis of furfural and HMF detoxification by pure and mixed batch cultures of S. cerevisiae and S. stipitis.

Inhibitory compounds that result from biomass hydrolysis are an obstacle to the efficient production of second-generation biofuels. Fermentative microorganisms can reduce compounds such as furfural and 5-hydroxymethyl furfural (HMF), but detoxification is accompanied by reduced growth rates and ethanol yields. In this study, we assess the effects of these furan aldehydes on pure and mixed yeast cultures consisting of a respiratory deficient mutant of Saccharomyces cerevisiae and wild-type Scheffersomyces stipitis using dynamic flux balance analysis. Uptake kinetics and stoichiometric equations for the intracellular reduction reactions associated with each inhibitor were added to genome-scale metabolic reconstructions of the two yeasts. Further modification of the S. cerevisiae metabolic network was necessary to satisfactorily predict the amount of acetate synthesized during HMF reduction. Inhibitory terms that captured the adverse effects of the furan aldehydes and their corresponding alcohols on cell growth and ethanol production were added to attain qualitative agreement with batch experiments conducted for model development and validation. When the two yeasts were co-cultured in the presence of the furan aldehydes, inoculums that reduced the synthesis of highly toxic acetate produced by S. cerevisiae yielded the highest ethanol productivities. The model described here can be used to generate optimal fermentation strategies for the simultaneous detoxification and fermentation of lignocellulosic hydrolysates by S. cerevisiae and/or S. stipitis.

Dynamic flux balance modeling of S. cerevisiae and E. coli co-cultures for efficient consumption of glucose/xylose mixtures.

Current researches into the production of biochemicals from lignocellulosic feedstocks are focused on the identification and engineering of individual microbes that utilize complex sugar mixtures. Microbial consortia represent an alternative approach that has the potential to better exploit individual species capabilities for substrate uptake and biochemical production. In this work, we construct and experimentally validate a dynamic flux balance model of a Saccharomyces cerevisiae and Escherichia coli co-culture designed for efficient aerobic consumption of glucose/xylose mixtures. Each microbe is a substrate specialist, with wild-type S. cerevisiae consuming only glucose and engineered E. coli strain ZSC113 consuming only xylose, to avoid diauxic growth commonly observed in individual microbes. Following experimental identification of a common pH and temperature for optimal co-culture batch growth, we demonstrate that pure culture models developed for optimal growth conditions can be adapted to the suboptimal, common growth condition by adjustment of the non-growth associated ATP maintenance of each microbe. By comparing pure culture model predictions to co-culture experimental data, the inhibitory effect of ethanol produced by S. cerevisiae on E. coli growth was found to be the only interaction necessary to include in the co-culture model to generate accurate batch profile predictions. Co-culture model utility was demonstrated by predicting initial cell concentrations that yield simultaneous glucose and xylose exhaustion for different sugar mixtures. Successful experimental validation of the model predictions demonstrated that steady-state metabolic reconstructions developed for individual microbes can be adapted to develop dynamic flux balance models of microbial consortia for the production of renewable chemicals.

Dynamic flux balance modeling of microbial co-cultures for efficient batch fermentation of glucose and xylose mixtures.

Sequential uptake of pentose and hexose sugars that compose lignocellulosic biomass limits the ability of pure microbial cultures to efficiently produce value-added bioproducts. In this work, we used dynamic flux balance modeling to examine the capability of mixed cultures of substrate-selective microbes to improve the utilization of glucose/xylose mixtures and to convert these mixed substrates into products. Co-culture simulations of Escherichia coli strains ALS1008 and ZSC113, engineered for glucose and xylose only uptake respectively, indicated that improvements in batch substrate consumption observed in previous experimental studies resulted primarily from an increase in ZSC113 xylose uptake relative to wild-type E. coli. The E. coli strain ZSC113 engineered for the elimination of glucose uptake was computationally co-cultured with wild-type Saccharomyces cerevisiae, which can only metabolize glucose, to determine if the co-culture was capable of enhanced ethanol production compared to pure cultures of wild-type E. coli and the S. cerevisiae strain RWB218 engineered for combined glucose and xylose uptake. Under the simplifying assumption that both microbes grow optimally under common environmental conditions, optimization of the strain inoculum and the aerobic to anaerobic switching time produced an almost twofold increase in ethanol productivity over the pure cultures. To examine the effect of reduced strain growth rates at non-optimal pH and temperature values, a break even analysis was performed to determine possible reductions in individual strain substrate uptake rates that resulted in the same predicted ethanol productivity as the best pure culture.

Dynamic flux balance analysis for synthetic microbial communities.

Dynamic flux balance analysis (DFBA) is an extension of classical flux balance analysis that allows the dynamic effects of the extracellular environment on microbial metabolism to be predicted and optimised. Recently this computational framework has been extended to microbial communities for which the individual species are known and genome-scale metabolic reconstructions are available. In this review, the authors provide an overview of the emerging DFBA approach with a focus on two case studies involving the conversion of mixed hexose/pentose sugar mixtures by synthetic microbial co-culture systems. These case studies illustrate the key requirements of the DFBA approach, including the incorporation of individual species metabolic reconstructions, formulation of extracellular mass balances, identification of substrate uptake kinetics, numerical solution of the coupled linear program/differential equations and model adaptation for common, suboptimal growth conditions and identified species interactions. The review concludes with a summary of progress to date and possible directions for future research.

The insertion Green Monster (iGM) method for expression of multiple exogenous genes in yeast.

Being a simple eukaryotic organism, Saccharomyces cerevisiae provides numerous advantages for expression and functional characterization of proteins from higher eukaryotes, including humans. However, studies of complex exogenous pathways using yeast as a host have been hampered by the lack of tools to engineer strains expressing a large number of genetic components. In addition to inserting multiple genes, it is often desirable to knock out or replace multiple endogenous genes that might interfere with the processes studied. Here, we describe the "insertion Green Monster" (iGM) set of expression vectors that enable precise insertion of many heterologous genes into the yeast genome in a rapid and reproducible manner and permit simultaneous replacement of selected yeast genes. As a proof of principle, we have used the iGM method to replace components of the yeast pathway for methionine sulfoxide reduction with genes encoding the human selenoprotein biosynthesis machinery and generated a single yeast strain carrying 11 exogenous components of the selenoprotein biosynthetic pathway in precisely engineered loci.

Dynamic metabolic modeling of a microaerobic yeast co-culture: predicting and optimizing ethanol production from glucose/xylose mixtures.

BACKGROUND: A key step in any process that converts lignocellulose to biofuels is the efficient fermentation of both hexose and pentose sugars. The co-culture of respiratory-deficient Saccharomyces cerevisiae and wild-type Scheffersomyces stipitis has been identified as a promising system for microaerobic ethanol production because S. cerevisiae only consumes glucose while S. stipitis efficiently converts xylose to ethanol. RESULTS: To better predict how these two yeasts behave in batch co-culture and to optimize system performance, a dynamic flux balance model describing co-culture metabolism was developed from genome-scale metabolic reconstructions of the individual organisms. First a dynamic model was developed for each organism by estimating substrate uptake kinetic parameters from batch pure culture data and evaluating model extensibility to different microaerobic growth conditions. The co-culture model was constructed by combining the two individual models assuming a cellular objective of total growth rate maximization. To obtain accurate predictions of batch co-culture data collected at different microaerobic conditions, the S. cerevisiae maximum glucose uptake rate was reduced from its pure culture value to account for more efficient S. stipitis glucose uptake in co-culture. The dynamic co-culture model was used to predict the inoculum concentration and aeration level that maximized batch ethanol productivity. The model predictions were validated with batch co-culture experiments performed at the optimal conditions. Furthermore, the dynamic model was used to predict how engineered improvements to the S. stipitis xylose transport system could improve co-culture ethanol production. CONCLUSIONS: These results demonstrate the utility of the dynamic co-culture metabolic model for guiding process and metabolic engineering efforts aimed at increasing microaerobic ethanol production from glucose/xylose mixtures.