Cong T. Trinh

Minimal Escherichia coli Cell for the Most Efficient Production of Ethanol from Hexoses and Pentoses

ABSTRACT To obtain an efficient ethanologenic Escherichia coli strain, we reduced the functional space of the central metabolic network, with eight gene knockout mutations, from over 15,000 pathway possibilities to 6 pathway options that support cell function. The remaining pathways, identified by elementary mode analysis, consist of four pathways with non-growth-associated conversion of pentoses and hexoses into ethanol at theoretical yields and two pathways with tight coupling of anaerobic cell growth with ethanol formation at high yields. Elimination of three additional genes resulted in a strain that selectively grows only on pentoses, even in the presence of glucose, with a high ethanol yield. We showed that the ethanol yields of strains with minimized metabolic functionality closely matched the theoretical predictions. Remarkably, catabolite repression was completely absent during anaerobic growth, resulting in the simultaneous utilization of pentoses and hexoses for ethanol production.

Elementary mode analysis: a useful metabolic pathway analysis tool for characterizing cellular metabolism

Elementary mode analysis is a useful metabolic pathway analysis tool to identify the structure of a metabolic network that links the cellular phenotype to the corresponding genotype. The analysis can decompose the intricate metabolic network comprised of highly interconnected reactions into uniquely organized pathways. These pathways consisting of a minimal set of enzymes that can support steady state operation of cellular metabolism represent independent cellular physiological states. Such pathway definition provides a rigorous basis to systematically characterize cellular phenotypes, metabolic network regulation, robustness, and fragility that facilitate understanding of cell physiology and implementation of metabolic engineering strategies. This mini-review aims to overview the development and application of elementary mode analysis as a metabolic pathway analysis tool in studying cell physiology and as a basis of metabolic engineering.

Metabolic engineering of Escherichia coli for efficient conversion of glycerol to ethanol

ABSTRACT Based on elementary mode analysis, an Escherichia coli strain was designed for efficient conversion of glycerol to ethanol. By using nine gene knockout mutations, the functional space of the central metabolism of E. coli was reduced from over 15,000 possible pathways to a total of 28 glycerol-utilizing pathways that support cell function. Among these pathways are eight aerobic and eight anaerobic pathways that do not support cell growth but convert glycerol into ethanol with a theoretical yield of 0.50 g ethanol/g glycerol. The remaining 12 pathways aerobically coproduce biomass and ethanol from glycerol. The optimal ethanol production depends on the oxygen availability that regulates the two competing pathways for biomass and ethanol production. The coupling between cell growth and ethanol production enabled metabolic evolution of the designed strain through serial dilution that resulted in strains with improved ethanol yields and productivities. In defined medium, the evolved strain can convert 40 g/liter of glycerol to ethanol in 48 h with 90% of the theoretical ethanol yield. The performance of the designed strain is predicted by the property space of remaining elementary modes.

Redesigning Escherichia coli Metabolism for Anaerobic Production of Isobutanol

ABSTRACT Fermentation enables the production of reduced metabolites, such as the biofuels ethanol and butanol, from fermentable sugars. This work demonstrates a general approach for designing and constructing a production host that uses a heterologous pathway as an obligately fermentative pathway to produce reduced metabolites, specifically, the biofuel isobutanol. Elementary mode analysis was applied to design an Escherichia coli strain optimized for isobutanol production under strictly anaerobic conditions. The central metabolism of E. coli was decomposed into 38,219 functional, unique, and elementary modes (EMs). The model predictions revealed that during anaerobic growth E. coli cannot produce isobutanol as the sole fermentative product. By deleting 7 chromosomal genes, the total 38,219 EMs were constrained to 12 EMs, 6 of which can produce high yields of isobutanol in a range from 0.29 to 0.41 g isobutanol/g glucose under anaerobic conditions. The remaining 6 EMs rely primarily on the pyruvate dehydrogenase enzyme complex (PDHC) and are typically inhibited under anaerobic conditions. The redesigned E. coli strain was constrained to employ the anaerobic isobutanol pathways through deletion of 7 chromosomal genes, addition of 2 heterologous genes, and overexpression of 5 genes. Here we present the design, construction, and characterization of an isobutanol-producing E. coli strain to illustrate the approach. The model predictions are evaluated in relation to experimental data and strategies proposed to improve anaerobic isobutanol production. We also show that the endogenous alcohol/aldehyde dehydrogenase AdhE is the key enzyme responsible for the production of isobutanol and ethanol under anaerobic conditions. The glycolytic flux can be controlled to regulate the ratio of isobutanol to ethanol production.

Rational design and construction of an efficient E. coli for production of diapolycopendioic acid

We applied elementary mode analysis to a recombinant metabolic network of carotenoid-producing E. coli in order to identify multiple-gene knockouts for an enhanced synthesis of the carotenoids diapolycopendial (DPL) and diapolycopendioic acid (DPA). Based on the model, all inefficient carotenoid biosynthesis pathways were eliminated in a strain containing a combination of eight gene deletions. To validate the model prediction, the designed strain was constructed and tested for its performance. The designed mutant produces the carotenoids at significantly increased yields and rates as compared to the wild-type. The consistency between model prediction and experimental results demonstrates that elementary mode analysis is useful as a guiding tool also for the rational strain design of more complex pathways for secondary metabolite production.

Enhancing fatty acid ethyl ester production in Saccharomyces cerevisiae through metabolic engineering and medium optimization

Biodiesels in the form of fatty acyl ethyl esters (FAEEs) are a promising next generation biofuel due to their chemical properties and compatibility with existing infrastructure. It has recently been shown that expression of a bacterial acyl‐transferase in the established industrial workhorse Saccharomyces cerevisiae can lead to production of FAEEs by condensation of fatty acyl‐CoAs and ethanol. In contrast to recent strategies to produce FAEEs in S. cerevisiae through manipulation of de novo fatty acid biosynthesis or a series of arduous genetic manipulations, we introduced a novel genetic background, which is comparable in titer to previous reports with a fraction of the genetic disruption by aiming at increasing the fatty acyl‐CoA pools. In addition, we combined metabolic engineering with modification of culture conditions to produce a maximum titer of over 25 mg/L FAEEs, a 40% improvement over previous reports and a 17‐fold improvement over our initial characterizations. Biotechnol. Bioeng. 2014;111: 2200–2208.

Elucidating and reprogramming Escherichia coli metabolisms for obligate anaerobic n-butanol and isobutanol production

Elementary mode (EM) analysis based on the constraint-based metabolic network modeling was applied to elucidate and compare complex fermentative metabolisms of Escherichia coli for obligate anaerobic production of n-butanol and isobutanol. The result shows that the n-butanol fermentative metabolism was NADH-deficient, while the isobutanol fermentative metabolism was NADH redundant. E. coli could grow and produce n-butanol anaerobically as the sole fermentative product but not achieve the maximum theoretical n-butanol yield. In contrast, for the isobutanol fermentative metabolism, E. coli was required to couple with either ethanol- or succinate-producing pathway to recycle NADH. To overcome these “defective” metabolisms, EM analysis was implemented to reprogram the native fermentative metabolism of E. coli for optimized anaerobic production of n-butanol and isobutanol through multiple gene deletion (∼8–9 genes), addition (∼6–7 genes), up- and downexpression (∼6–7 genes), and cofactor engineering (e.g., NADH, NADPH). The designed strains were forced to couple both growth and anaerobic production of n-butanol and isobutanol, which is a useful characteristic to enhance biofuel production and tolerance through metabolic pathway evolution. Even though the n-butanol and isobutanol fermentative metabolisms were quite different, the designed strains could be engineered to have identical metabolic flux distribution in “core” metabolic pathways mainly supporting cell growth and maintenance. Finally, the model prediction in elucidating and reprogramming the native fermentative metabolism of E. coli for obligate anaerobic production of n-butanol and isobutanol was validated with published experimental data.

Activating and Elucidating Metabolism of Complex Sugars in Yarrowia lipolytica

ABSTRACT The oleaginous yeast Yarrowia lipolytica is an industrially important host for production of organic acids, oleochemicals, lipids, and proteins with broad biotechnological applications. Albeit known for decades, the unique native metabolism of Y. lipolytica for using complex fermentable sugars, which are abundant in lignocellulosic biomass, is poorly understood. In this study, we activated and elucidated the native sugar metabolism in Y. lipolytica for cell growth on xylose and cellobiose as well as their mixtures with glucose through comprehensive metabolic and transcriptomic analyses. We identified 7 putative glucose-specific transporters, 16 putative xylose-specific transporters, and 4 putative cellobiose-specific transporters that are transcriptionally upregulated for growth on respective single sugars. Y. lipolytica is capable of using xylose as a carbon source, but xylose dehydrogenase is the key bottleneck of xylose assimilation and is transcriptionally repressed by glucose. Y. lipolytica has a set of 5 extracellular and 6 intracellular β-glucosidases and is capable of assimilating cellobiose via extra- and intracellular mechanisms, the latter being dominant for growth on cellobiose as a sole carbon source. Strikingly, Y. lipolytica exhibited enhanced sugar utilization for growth in mixed sugars, with strong carbon catabolite activation for growth on the mixture of xylose and cellobiose and with mild carbon catabolite repression of glucose on xylose and cellobiose. The results of this study shed light on fundamental understanding of the complex native sugar metabolism of Y. lipolytica and will help guide inverse metabolic engineering of Y. lipolytica for enhanced conversion of biomass-derived fermentable sugars to chemicals and fuels.

Parallelization of Nullspace Algorithm for the computation of metabolic pathways.

Elementary mode analysis is a useful metabolic pathway analysis tool in understanding and analyzing cellular metabolism, since elementary modes can represent metabolic pathways with unique and minimal sets of enzyme-catalyzed reactions of a metabolic network under steady state conditions. However, computation of the elementary modes of a genome- scale metabolic network with 100-1000 reactions is very expensive and sometimes not feasible with the commonly used serial Nullspace Algorithm. In this work, we develop a distributed memory parallelization of the Nullspace Algorithm to handle efficiently the computation of the elementary modes of a large metabolic network. We give an implementation in C++ language with the support of MPI library functions for the parallel communication. Our proposed algorithm is accompanied with an analysis of the complexity and identification of major bottlenecks during computation of all possible pathways of a large metabolic network. The algorithm includes methods to achieve load balancing among the compute-nodes and specific communication patterns to reduce the communication overhead and improve efficiency.

SMET: systematic multiple enzyme targeting - a method to rationally design optimal strains for target chemical overproduction.

Identifying multiple enzyme targets for metabolic engineering is very critical for redirecting cellular metabolism to achieve desirable phenotypes, e.g., overproduction of a target chemical. The challenge is to determine which enzymes and how much of these enzymes should be manipulated by adding, deleting, under-, and/or over-expressing associated genes. In this study, we report the development of a systematic multiple enzyme targeting method (SMET), to rationally design optimal strains for target chemical overproduction. The SMET method combines both elementary mode analysis and ensemble metabolic modeling to derive SMET metrics including l-values and c-values that can identify rate-limiting reaction steps and suggest which enzymes and how much of these enzymes to manipulate to enhance product yields, titers, and productivities. We illustrated, tested, and validated the SMET method by analyzing two networks, a simple network for concept demonstration and an Escherichia coli metabolic network for aromatic amino acid overproduction. The SMET method could systematically predict simultaneous multiple enzyme targets and their optimized expression levels, consistent with experimental data from the literature, without performing an iterative sequence of single-enzyme perturbation. The SMET method was much more efficient and effective than single-enzyme perturbation in terms of computation time and finding improved solutions.