Design Of Metabolic Pathways Research Articles

Design and analysis of metabolic pathways supporting formatotrophic growth for electricity-dependent cultivation of microbes

Electrosynthesis is a promising approach that enables the biological production of commodities, like fuels and fine chemicals, using renewably produced electricity. Several techniques have been proposed to mediate the transfer of electrons from the cathode to living cells. Of these, the electroproduction of formate as a mediator seems especially promising: formate is readily soluble, of low toxicity and can be produced at relatively high efficiency and at reasonable current density. While organisms that are capable of formatotrophic growth, i.e. growth on formate, exist naturally, they are generally less suitable for bulk cultivation and industrial needs. Hence, it may be helpful to engineer a model organism of industrial relevance, such as E. coli, for growth on formate. There are numerous metabolic pathways that can potentially support formatotrophic growth. Here we analyze these diverse pathways according to various criteria including biomass yield, thermodynamic favorability, chemical motive force, kinetics and the practical challenges posed by their expression. We find that the reductive glycine pathway, composed of the tetrahydrofolate system, the glycine cleavage system, serine hydroxymethyltransferase and serine deaminase, is a promising candidate to support electrosynthesis in E. coli. The approach presented here exemplifies how combining different computational approaches into a systematic analysis methodology provides assistance in redesigning metabolism. This article is part of a Special Issue entitled: Metals in Bioenergetics and Biomimetics Systems.

Enrichment of Error‐Free Synthetic DNA Sequences by CEL I Nuclease

As the availability of DNA sequence information has grown, so has the need to replicate DNA sequences synthetically. Synthetically produced DNA sequences allow the researcher to exert greater control over model systems and allow for the combinatorial design and construction of novel metabolic and regulatory pathways, as well as optimized protein-coding sequences for biotechnological applications. This utility has made synthetically produced DNA a hallmark of the molecular biosciences and a mainstay of synthetic biology. However, synthetically produced DNA has a significant shortcoming in that it typically has an error rate that is orders of magnitude higher when compared to DNA sequences derived directly from a biological source. This relatively high error rate adds to the cost and labor necessary to obtain sequence-verified clones from synthetically produced DNA sequences. This unit describes a protocol to enrich error-free sequences from a population of error-rich DNA via treatment with CEL I (Surveyor) endonuclease. This method is a straightforward and quick way of reducing the error content of synthetic DNA pools and reliably reduces the error rates by >6-fold per round of treatment.

EPathBrick: A Synthetic Biology Platform for Engineering Metabolic Pathways in E. coli

Harnessing cell factories for producing biofuel and pharmaceutical molecules has stimulated efforts to develop novel synthetic biology tools customized for modular pathway engineering and optimization. Here we report the development of a set of vectors compatible with BioBrick standards and its application in metabolic engineering. The engineered ePathBrick vectors comprise four compatible restriction enzyme sites allocated on strategic positions so that different regulatory control signals can be reused and manipulation of expression cassette can be streamlined. Specifically, these vectors allow for fine-tuning gene expression by integrating multiple transcriptional activation or repression signals into the operator region. At the same time, ePathBrick vectors support the modular assembly of pathway components and combinatorial generation of pathway diversities with three distinct configurations. We also demonstrated the functionality of a seven-gene pathway (~9 Kb) assembled on one single ePathBrick vector. The ePathBrick vectors presented here provide a versatile platform for rapid design and optimization of metabolic pathways in E. coli.

Abstract 3210: Glutamine synthetase is a genetic determinant of cell-type specific glutamine independence in breast epithelia

Abstract Although significant variations in the metabolic profiles exist among different cells, little is understood in terms of genetic regulations of such cell-type specific metabolic phenotypes and nutrient requirement. While many cancer cells depend on exogenous glutamine for survival to justify the therapeutic targeting of glutamine metabolism, the mechanisms of glutamine dependence and likely response and resistance of such glutamine-targeting strategies among cancers are largely unknown. In this study, we have found a systematic variation in the glutamine dependence among breast tumor subtypes associated with mammary differentiation: basal but not luminal-type breast cells are more glutamine dependent and may be susceptible to glutamine-targeting therapeutics. Glutamine independence of luminal-type cells is associated mechanistically with lineage-specific expression of glutamine synthetase (GS). Luminal cells can also rescue basal cells in co-culture without glutamine, indicating a potential for glutamine symbiosis within breast ducts. The luminal-specific expression of GS is directly induced by GATA3 and represses glutaminase expression. Such distinct glutamine dependency and metabolic symbiosis is coupled with the acquisition of the GS and glutamine independence during the mammary differentiation program. Understanding the genetic circuitry governing distinct metabolic patterns is relevant to many symbiotic relationships among different cells and organisms. In addition, the ability of GS to predict patterns of glutamine metabolism and dependency among tumors is also crucial in the rational design and application of glutamine and other metabolic pathway targeted therapies. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3210. doi:1538-7445.AM2012-3210

Compound toxicity screening and structure–activity relationship modeling in Escherichia coli

Synthetic biology and metabolic engineering are used to develop new strategies for producing valuable compounds ranging from therapeutics to biofuels in engineered microorganisms. When developing methods for high-titer production cells, toxicity is an important element to consider. Indeed the production rate can be limited due to toxic intermediates or accumulation of byproducts of the heterologous biosynthetic pathway of interest. Conversely, highly toxic molecules are desired when designing antimicrobials. Compound toxicity in bacteria plays a major role in metabolic engineering as well as in the development of new antibacterial agents. Here, we screened a diversified chemical library of 166 compounds for toxicity in Escherichia coli. The dataset was built using a clustering algorithm maximizing the chemical diversity in the library. The resulting assay data was used to develop a toxicity predictor that we used to assess the toxicity of metabolites throughout the metabolome. This new tool for predicting toxicity can thus be used for fine-tuning heterologous expression and can be integrated in a computational-framework for metabolic pathway design. Many structure-activity relationship tools have been developed for toxicology studies in eukaryotes [Valerio (2009), Toxicol Appl Pharmacol, 241(3): 356-370], however, to the best of our knowledge we present here the first E. coli toxicity prediction web server based on QSAR models (EcoliTox server: http://www.issb.genopole.fr/∼faulon/EcoliTox.php).

Glutamine Synthetase Is a Genetic Determinant of Cell Type–Specific Glutamine Independence in Breast Epithelia

Although significant variations in the metabolic profiles exist among different cells, little is understood in terms of genetic regulations of such cell type–specific metabolic phenotypes and nutrient requirements. While many cancer cells depend on exogenous glutamine for survival to justify the therapeutic targeting of glutamine metabolism, the mechanisms of glutamine dependence and likely response and resistance of such glutamine-targeting strategies among cancers are largely unknown. In this study, we have found a systematic variation in the glutamine dependence among breast tumor subtypes associated with mammary differentiation: basal- but not luminal-type breast cells are more glutamine-dependent and may be susceptible to glutamine-targeting therapeutics. Glutamine independence of luminal-type cells is associated mechanistically with lineage-specific expression of glutamine synthetase (GS). Luminal cells can also rescue basal cells in co-culture without glutamine, indicating a potential for glutamine symbiosis within breast ducts. The luminal-specific expression of GS is directly induced by GATA3 and represses glutaminase expression. Such distinct glutamine dependency and metabolic symbiosis is coupled with the acquisition of the GS and glutamine independence during the mammary differentiation program. Understanding the genetic circuitry governing distinct metabolic patterns is relevant to many symbiotic relationships among different cells and organisms. In addition, the ability of GS to predict patterns of glutamine metabolism and dependency among tumors is also crucial in the rational design and application of glutamine and other metabolic pathway targeted therapies.

A retrosynthetic biology approach to metabolic pathway design for therapeutic production

BackgroundSynthetic biology is used to develop cell factories for production of chemicals by constructively importing heterologous pathways into industrial microorganisms. In this work we present a retrosynthetic approach to the production of therapeutics with the goal of developing an in situ drug delivery device in host cells. Retrosynthesis, a concept originally proposed for synthetic chemistry, iteratively applies reversed chemical transformations (reversed enzyme-catalyzed reactions in the metabolic space) starting from a target product to reach precursors that are endogenous to the chassis. So far, a wider adoption of retrosynthesis into the manufacturing pipeline has been hindered by the complexity of enumerating all feasible biosynthetic pathways for a given compound.ResultsIn our method, we efficiently address the complexity problem by coding substrates, products and reactions into molecular signatures. Metabolic maps are represented using hypergraphs and the complexity is controlled by varying the specificity of the molecular signature. Furthermore, our method enables candidate pathways to be ranked to determine which ones are best to engineer. The proposed ranking function can integrate data from different sources such as host compatibility for inserted genes, the estimation of steady-state fluxes from the genome-wide reconstruction of the organism's metabolism, or the estimation of metabolite toxicity from experimental assays. We use several machine-learning tools in order to estimate enzyme activity and reaction efficiency at each step of the identified pathways. Examples of production in bacteria and yeast for two antibiotics and for one antitumor agent, as well as for several essential metabolites are outlined.ConclusionsWe present here a unified framework that integrates diverse techniques involved in the design of heterologous biosynthetic pathways through a retrosynthetic approach in the reaction signature space. Our engineering methodology enables the flexible design of industrial microorganisms for the efficient on-demand production of chemical compounds with therapeutic applications.

Prediction of novel synthetic pathways for the production of desired chemicals

BackgroundThere have been several methods developed for the prediction of synthetic metabolic pathways leading to the production of desired chemicals. In these approaches, novel pathways were predicted based on chemical structure changes, enzymatic information, and/or reaction mechanisms, but the approaches generating a huge number of predicted results are difficult to be applied to real experiments. Also, some of these methods focus on specific pathways, and thus are limited to expansion to the whole metabolism.ResultsIn the present study, we propose a system framework employing a retrosynthesis model with a prioritization scoring algorithm. This new strategy allows deducing the novel promising pathways for the synthesis of a desired chemical together with information on enzymes involved based on structural changes and reaction mechanisms present in the system database. The prioritization scoring algorithm employing Tanimoto coefficient and group contribution method allows examination of structurally qualified pathways to recognize which pathway is more appropriate. In addition, new concepts of binding site covalence, estimation of pathway distance and organism specificity were taken into account to identify the best synthetic pathway. Parameters of these factors can be evolutionarily optimized when a newly proven synthetic pathway is registered. As the proofs of concept, the novel synthetic pathways for the production of isobutanol, 3-hydroxypropionate, and butyryl-CoA were predicted. The prediction shows a high reliability, in which experimentally verified synthetic pathways were listed within the top 0.089% of the identified pathway candidates.ConclusionsIt is expected that the system framework developed in this study would be useful for the in silico design of novel metabolic pathways to be employed for the efficient production of chemicals, fuels and materials.

Rational bioprocess design and manipulation of metabolic pathways for zero- and one-carbon sustainable bioindustry

Rational bioprocess design and manipulation of metabolic pathways for zero- and one-carbon sustainable bioindustry

DESHARKY: automatic design of metabolic pathways for optimal cell growth

The biological solution for synthesis or remediation of organic compounds using living organisms, particularly bacteria and yeast, has been promoted because of the cost reduction with respect to the non-living chemical approach. In that way, computational frameworks can profit from the previous knowledge stored in large databases of compounds, enzymes and reactions. In addition, the cell behavior can be studied by modeling the cellular context. We have implemented a Monte Carlo algorithm (DESHARKY) that finds a metabolic pathway from a target compound by exploring a database of enzymatic reactions. DESHARKY outputs a biochemical route to the host metabolism together with its impact in the cellular context by using mathematical models of the cell resources and metabolism. Furthermore, we provide the sequence of amino acids for the enzymes involved in the route closest phylogenetically to the considered organism. We provide examples of designed metabolic pathways with their genetic load characterizations. Here, we have used Escherichia coli as host organism. In addition, our bioinformatic tool can be applied for biodegradation or biosynthesis and its performance scales with the database size. Software, a tutorial and examples are freely available and open source at http://soft.synth-bio.org/desharky.html

Innovative metabolic pathway design for efficient l-glutamate production by suppressing CO2 emission

In the pathway of L-glutamic acid (L-Glu) biosynthesis in Corynebacterium glutamicum, 1 mol of L-Glu is synthesized from 1 mol of glucose at a cost of 1 mol of carbon dioxide (CO(2)), with a maximum theoretical yield of 81.7% by weight. We have designed an innovative pathway for efficient L-Glu production employing phosphoketolase (PKT) to bypass the CO(2)-releasing pyruvate dehydrogenase reaction, thereby increasing the maximum theoretical yield of L-Glu from glucose to up to 98.0% by weight (120% mol/mol L-Glu produced/glucose consumed). The xfp gene encoding PKT was cloned from Bifidobacterium animalis and overexpressed under the strong cspB promoter in C. glutamicum. A functional enzyme was detected in an L-Glu-producing strain of C. glutamicum (odhA). When cells of this producer strain with the xfp gene and those without the xfp gene were cultivated in a controlled fermentation system, the L-Glu production yield of the strain expressing the xfp gene was much higher than that of the original strain, coupled with the suppression of CO(2) emission. Consequently, we could successfully enhance L-glutamate production by installing the PKT pathway of B. animalis into C. glutamicuml-Glu metabolism, and this novel metabolic design will be able to increase L-Glu production yield beyond the maximum theoretical yield obtained from the conventional metabolic pathway of biosynthesis from glucose.

Why is metabolic labour divided in nitrification?

Winogradsky discovered in 1890 that nitrification is carried out in two consecutive steps by two distinct groups of bacteria: ammonia-oxidizing bacteria and nitrite-oxidizing bacteria. An explanation for this division of labour is offered based on the kinetic theory of optimal design of metabolic pathways, which postulates the existence of an optimal length for a pathway that maximizes the rate of ATP production. Shortening long pathways could, therefore, increase growth rate. However, this would reduce growth yield if the shorter pathway has fewer ATP-generating steps. High yields would be advantageous when bacteria grow in clonal clusters, as is typical for biofilms. It is postulated that bacteria that completely oxidize ammonia to nitrate exist in such environments.

Integration of open source software tools for the in silico design of metabolic pathways using flux balance analysis – an application to the production of recombinant human proteins

This study describes the development of a system that calculates the fluxes of a metabolic network. It integrates several open source software tools in order to simulate the behaviour of specific metabolic pathways using Flux Balance Analysis (FBA). This tool receives as input the stoichiometry, the thermodynamic and capacity constraints of the metabolic network, and also an objective function.

Biotechnological production and applications of coenzyme Q10

Coenzyme Q10 is widely used as an essential component of ATP generation in the oxidative phosphorylation process and as an antioxidant preventing lipid peroxidation and scavenging superoxide. It is also recommended as a supplement to 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors. Research efforts on the production of coenzyme Q10 by microorganisms focus on the development of potent strains by conventional mutagenesis and metabolic engineering, analysis and modification of the key metabolic pathways and optimization of fermentation strategies. Especially, random mutants with drugs resistance show a high coenzyme Q10 concentration. Metabolic engineering techniques have been applied to improve coenzyme Q10 production. The key enzymes involved in the coenzyme Q10 biosynthesis pathway have been cloned and expressed in Escherichia coli. The rational design of metabolic pathways in combination with engineering optimization of fermentation processes could facilitate the development of viable bioconversion processes.

Production of Amino Acids: Physiological and Genetic Approaches

Large scale fermentative production of l-amino acids is still an important goal of modern biotechnology. Very large amounts of l-glutamate and l-lysine, as well as significant quantities of l-threonine and l-phenylalanine and other amino acids for the application in food, feed, and for pharmaceutical purposes are currently produced by fermentation using mainly the two organisms Corynebacterium glutamicum and Escherichia coli. In the past 50 years, development of producing strains initially depended mostly on classical strain breeding involving repeated random mutation and selection. The development of modern tools of molecular biology enabled more rational approaches to strain improvement. The purposeful design of metabolic pathways, transport functions, and regulatory mechanisms in order to increase the yield and productivity of amino acid producing strains is based on the knowledge of fundamental aspects of physiology, biochemistry, molecular biology, and bioprocess engineering of the relevant organisms in general and of specific producing strains in particular. In addition to genome sequencing, modern techniques of transcriptome, proteome, metabolome, and metabolic flux analysis have recently been introduced in order to identify new and important target genes and to quantify metabolic activities. The fundamental physiological and biochemical principles relevant for amino acid production as well as modern developments in the direction of a purposeful metabolic design will be summarized.

In silico metabolic pathway analysis and design: succinic acid production by metabolically engineered Escherichia coli as an example.

The intracellular metabolic fluxes can be calculated by metabolic flux analysis, which uses a stoichiometric model for the intracellular reactions along with mass balances around the intracellular metabolites. In this study, we have constructed in silico metabolic pathway network of Escherichia coli consisting of 301 reactions and 294 metabolites. Metabolic flux analyses were carried out to estimate flux distributions to achieve the maximum in silico yield of succinic acid in E. coli. The maximum in silico yield of succinic acid was only 83% of its theoretical yield. The lower in silico yield of succinic acid was found to be due to the insufficient reducing power, which could be increased to its theoretical yield by supplying more reducing power. Furthermore, the optimal metabolic pathways for the production of succinic acid could be proposed based on the results of metabolic flux analyses. In the case of succinic acid production, it was found that pyruvate carboxylation pathway should be used rather than phosphoenolpyruvate carboxylation pathway for its optimal production in E. coli. Then, the in silico optimal succinic acid pathway was compared with conventional succinic acid pathway through minimum set of wet experiments. The results of wet experiments indicate that the pathway predicted by in silico analysis is more efficient than conventional pathway.

Precursor balancing for metabolic engineering of lycopene production in Escherichia coli.

One issue that must be addressed in the rational design of metabolic pathways is the elimination of potential bottlenecks in the upstream pathways. We have reconstructed the isoprenoid pathway to overproduce the carotenoid lycopene in Escherichia coli. Here we show that the distribution between pyruvate and glyceraldehyde 3-phosphate (G3P), the originating precursors of the isoprenoid pathway, is a major factor that can limit isoprenoid production yields in E. coli. In particular, alterations in the central metabolism that redirect flux from pyruvate back to G3P enhance lycopene production, while alterations that channel carbon flux away from the G3P pool have the opposite effect. These results suggest that G3P may be limiting in the biosynthesis of lycopene, and modifications that achieve a more equitable distribution between the two precursors are able to increase the lycopene yield in metabolically engineered E. coli.

Optimal Stoichiometric Designs of ATP-producing Systems as Determined by an Evolutionary Algorithm

The design of metabolic pathways is thought to be the result of an optimization process such that the structure of contemporary metabolic routes maximizes a particular objective function. Recently, it has been shown that some essential stoichiometric properties of glycolysis can be explained on the basis of the requirement for a high ATP production rate. Because the number of stoichiometrically feasible designs increases strongly with the number of reactions involved, a systematic analysis of all the possibilities turns out to be inaccessible beyond a certain system size. We present, therefore, an alternative approach to compute in a more efficient way the optimal design of glycolysis interacting with an external ATP-consuming reaction. The algorithm is based on the laws of evolution by natural selection, and may be viewed as a particular version of evolutionary algorithms. The following conclusions are derived: (a) evolutionary algorithms are very useful search strategies in determining optimal stoichiometries of metabolic pathways. (b) Essential topological features of the glycolytic network may be explained on the basis of flux optimization. (c) There is a strong interrelation between the optimal stoichiometries and the thermodynamic and kinetic properties of the participating reactions. (d) Some subsequences of reactions in optimal pathways are strongly conserved at variation of system parameters, which may be understood by applying principles of metabolic control analysis.

The modelling of metabolic systems. Structure, control and optimality

This article gives an overview of recent developments in the modelling of the structure, control and optimality of metabolic networks. In particular, methods of algebraically analysing the topology of such networks are presented. By these methods, conservation relations and elementary modes of functioning (biochemical routes) can be detected. The principles of metabolic control analysis are outlined. Various recent extensions of this theory are presented, such as an analysis in terms of time dependent variables and modular analysis. Evolutionary optimisation principles are applied to explain the catalytic efficiency of single enzymes as well as the structural design of metabolic pathways. Special results concern the optimal distribution of ATP consuming and ATP producing reactions in glycolysis.

Optimization and Control of Metabolic Systems

Evolutionary optimization principles are applied to explain the catalytic efficiency of single enzymes as well as the structural design of metabolic pathways. Special results concern (1) the optimal values of elementary rate constants of enzymatic mechanisms as functions of the external substrate and product concentrations; (2) the distribution of enzyme concentrations in metabolic pathways for states of maximal fluxes, and (3) the optimal distribution of ATP consuming and ATP producing reactions in glycolysis. Conclusions are drawn for the distribution of flux control coefficients resulting from optimization processes.