Metabolic engineering

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Some of the steps in engineering a metabolic process Computational design and evaluation of DNA circuits to achieve optimal performance.svg
Some of the steps in engineering a metabolic process

Metabolic engineering is the practice of optimizing genetic and regulatory processes within cells to increase the cell's production of a certain substance. These processes are chemical networks that use a series of biochemical reactions and enzymes that allow cells to convert raw materials into molecules necessary for the cell's survival. Metabolic engineering specifically seeks to mathematically model these networks, calculate a yield of useful products, and pin point parts of the network that constrain the production of these products. [1] Genetic engineering techniques can then be used to modify the network in order to relieve these constraints. Once again this modified network can be modeled to calculate the new product yield.

Contents

The ultimate goal of metabolic engineering is to be able to use these organisms to produce valuable substances on an industrial scale in a cost-effective manner. Current examples include producing beer, wine, cheese, pharmaceuticals, and other biotechnology products. [2] Some of the common strategies used for metabolic engineering are (1) overexpressing the gene encoding the rate-limiting enzyme of the biosynthetic pathway, (2) blocking the competing metabolic pathways, (3) heterologous gene expression, and (4) enzyme engineering. [3]

Since cells use these metabolic networks for their survival, changes can have drastic effects on the cells' viability. Therefore, trade-offs in metabolic engineering arise between the cells ability to produce the desired substance and its natural survival needs. Therefore, instead of directly deleting and/or overexpressing the genes that encode for metabolic enzymes, the current focus is to target the regulatory networks in a cell to efficiently engineer the metabolism. [4]

History and applications

Cellular metabolism can be optimized for industrial use. Epithelial-cells.jpg
Cellular metabolism can be optimized for industrial use.

In the past, to increase the productivity of a desired metabolite, a microorganism was genetically modified by chemically induced mutation, and the mutant strain that overexpressed the desired metabolite was then chosen. [5] However, one of the main problems with this technique was that the metabolic pathway for the production of that metabolite was not analyzed, and as a result, the constraints to production and relevant pathway enzymes to be modified were unknown. [5]

In 1990s, a new technique called metabolic engineering emerged. This technique analyzes the metabolic pathway of a microorganism, and determines the constraints and their effects on the production of desired compounds. It then uses genetic engineering to relieve these constraints. Some examples of successful metabolic engineering are the following: (i) Identification of constraints to lysine production in Corynebacterium glutamicum and insertion of new genes to relieve these constraints to improve production [6] (ii) Engineering of a new fatty acid biosynthesis pathway, called reversed beta oxidation pathway, that is more efficient than the native pathway in producing fatty acids and alcohols which can potentially be catalytically converted to chemicals and fuels [7] (iii) Improved production of DAHP an aromatic metabolite produced by E. coli that is an intermediate in the production of aromatic amino acids. [8] It was determined through metabolic flux analysis that the theoretical maximal yield of DAHP per glucose molecule utilized, was 3/7. This is because some of the carbon from glucose is lost as carbon dioxide, instead of being utilized to produce DAHP. Also, one of the metabolites (PEP, or phosphoenolpyruvate) that are used to produce DAHP, was being converted to pyruvate (PYR) to transport glucose into the cell, and therefore, was no longer available to produce DAHP. In order to relieve the shortage of PEP and increase yield, Patnaik et al. used genetic engineering on E. coli to introduce a reaction that converts PYR back to PEP. Thus, the PEP used to transport glucose into the cell is regenerated, and can be used to make DAHP. This resulted in a new theoretical maximal yield of 6/7 – double that of the native E. coli system.

At the industrial scale, metabolic engineering is becoming more convenient and cost-effective. According to the Biotechnology Industry Organization, "more than 50 biorefinery facilities are being built across North America to apply metabolic engineering to produce biofuels and chemicals from renewable biomass which can help reduce greenhouse gas emissions". Potential biofuels include short-chain alcohols and alkanes (to replace gasoline), fatty acid methyl esters and fatty alcohols (to replace diesel), and fatty acid-and isoprenoid-based biofuels (to replace diesel). [9]

Metabolic engineering continues to evolve in efficiency and processes aided by breakthroughs in the field of synthetic biology and progress in understanding metabolite damage and its repair or preemption. Early metabolic engineering experiments showed that accumulation of reactive intermediates can limit flux in engineered pathways and be deleterious to host cells if matching damage control systems are missing or inadequate. [10] [11] Researchers in synthetic biology optimize genetic pathways, which in turn influence cellular metabolic outputs. Recent decreases in cost of synthesized DNA and developments in genetic circuits help to influence the ability of metabolic engineering to produce desired outputs. [12]

Metabolic flux analysis

An analysis of metabolic flux can be found at Flux balance analysis

Setting up a metabolic pathway for analysis

The first step in the process is to identify a desired goal to achieve through the improvement or modification of an organism's metabolism. Reference books and online databases are used to research reactions and metabolic pathways that are able to produce this product or result. These databases contain copious genomic and chemical information including pathways for metabolism and other cellular processes. Using this research, an organism is chosen that will be used to create the desired product or result. Considerations that are taken into account when making this decision are how close the organism's metabolic pathway is to the desired pathway, the maintenance costs associated with the organism, and how easy it is to modify the pathway of the organism. Escherichia coli (E. coli) is widely used in metabolic engineering to synthesize a wide variety of products such as amino acids because it is relatively easy to maintain and modify. [13] If the organism does not contain the complete pathway for the desired product or result, then genes that produce the missing enzymes must be incorporated into the organism.

Analyzing a metabolic pathway

The completed metabolic pathway is modeled mathematically to find the theoretical yield of the product or the reaction fluxes in the cell. A flux is the rate at which a given reaction in the network occurs. Simple metabolic pathway analysis can be done by hand, but most require the use of software to perform the computations. [14] These programs use complex linear algebra algorithms to solve these models. To solve a network using the equation for determined systems shown below, one must input the necessary information about the relevant reactions and their fluxes. Information about the reaction (such as the reactants and stoichiometry) are contained in the matrices Gx and Gm. Matrices Vm and Vx contain the fluxes of the relevant reactions. When solved, the equation yields the values of all the unknown fluxes (contained in Vx).

Determining the optimal genetic manipulations

After solving for the fluxes of reactions in the network, it is necessary to determine which reactions may be altered in order to maximize the yield of the desired product. To determine what specific genetic manipulations to perform, it is necessary to use computational algorithms, such as OptGene or OptFlux. [15] They provide recommendations for which genes should be overexpressed, knocked out, or introduced in a cell to allow increased production of the desired product. For example, if a given reaction has particularly low flux and is limiting the amount of product, the software may recommend that the enzyme catalyzing this reaction should be overexpressed in the cell to increase the reaction flux. The necessary genetic manipulations can be performed using standard molecular biology techniques. Genes may be overexpressed or knocked out from an organism, depending on their effect on the pathway and the ultimate goal. [16]

Experimental measurements

In order to create a solvable model, it is often necessary to have certain fluxes already known or experimentally measured. In addition, in order to verify the effect of genetic manipulations on the metabolic network (to ensure they align with the model), it is necessary to experimentally measure the fluxes in the network. To measure reaction fluxes, carbon flux measurements are made using carbon-13 isotopic labeling. [17] The organism is fed a mixture that contains molecules where specific carbons are engineered to be carbon-13 atoms, instead of carbon-12. After these molecules are used in the network, downstream metabolites also become labeled with carbon-13, as they incorporate those atoms in their structures. The specific labeling pattern of the various metabolites is determined by the reaction fluxes in the network. Labeling patterns may be measured using techniques such as gas chromatography-mass spectrometry (GC-MS) along with computational algorithms to determine reaction fluxes.

See also

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Metabolism is the set of life-sustaining chemical reactions in organisms. The three main purposes of metabolism are: the conversion of the energy in food to energy available to run cellular processes; the conversion of food to building blocks for proteins, lipids, nucleic acids, and some carbohydrates; and the elimination of metabolic wastes. These enzyme-catalyzed reactions allow organisms to grow and reproduce, maintain their structures, and respond to their environments. The word metabolism can also refer to the sum of all chemical reactions that occur in living organisms, including digestion and the transportation of substances into and between different cells, in which case the above described set of reactions within the cells is called intermediary metabolism.

Metabolic pathway Chemical reactions occurring within a cell

In biochemistry, a metabolic pathway is a linked series of chemical reactions occurring within a cell. The reactants, products, and intermediates of an enzymatic reaction are known as metabolites, which are modified by a sequence of chemical reactions catalyzed by enzymes. In most cases of a metabolic pathway, the product of one enzyme acts as the substrate for the next. However, side products are considered waste and removed from the cell. These enzymes often require dietary minerals, vitamins, and other cofactors to function.

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Synthetic biology (SynBio) is a multidisciplinary area of research that seeks to create new biological parts, devices, and systems, or to redesign systems that are already found in nature.

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Metabolic network modelling

Metabolic network modelling, also known as metabolic network reconstruction or metabolic pathway analysis, allows for an in-depth insight into the molecular mechanisms of a particular organism. In particular, these models correlate the genome with molecular physiology. A reconstruction breaks down metabolic pathways into their respective reactions and enzymes, and analyzes them within the perspective of the entire network. In simplified terms, a reconstruction collects all of the relevant metabolic information of an organism and compiles it in a mathematical model. Validation and analysis of reconstructions can allow identification of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. This knowledge can then be applied to create novel biotechnology.

KEGG Collection of bioinformatics databases

KEGG is a collection of databases dealing with genomes, biological pathways, diseases, drugs, and chemical substances. KEGG is utilized for bioinformatics research and education, including data analysis in genomics, metagenomics, metabolomics and other omics studies, modeling and simulation in systems biology, and translational research in drug development.

Flux balance analysis

Flux balance analysis (FBA) is a mathematical method for simulating metabolism in genome-scale reconstructions of metabolic networks. In comparison to traditional methods of modeling, FBA is less intensive in terms of the input data required for constructing the model. Simulations performed using FBA are computationally inexpensive and can calculate steady-state metabolic fluxes for large models in a few seconds on modern personal computers. The related method of metabolic pathway analysis seeks to find and list all possible pathways between metabolites.

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Amino acid synthesis

Amino acid synthesis is the set of biochemical processes by which the amino acids are produced. The substrates for these processes are various compounds in the organism's diet or growth media. Not all organisms are able to synthesize all amino acids. For example, humans can only synthesize 11 of the 20 standard amino acids, and in time of accelerated growth, histidine can be considered an essential amino acid.

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Metabolic flux analysis

Metabolic flux analysis (MFA) is an experimental fluxomics technique used to examine production and consumption rates of metabolites in a biological system. At an intracellular level, it allows for the quantification of metabolic fluxes, thereby elucidating the central metabolism of the cell. Various methods of MFA, including isotopically stationary metabolic flux analysis, isotopically non-stationary metabolic flux analysis, and thermodynamics-based metabolic flux analysis, can be coupled with stoichiometric models of metabolism and mass spectrometry methods with isotopic mass resolution to elucidate the transfer of moieties containing isotopic tracers from one metabolite into another and derive information about the metabolic network. Metabolic flux analysis (MFA) has many applications such as determining the limits on the ability of a biological system to produce a biochemical such as ethanol, predicting the response to gene knockout, and guiding the identification of bottleneck enzymes in metabolic networks for metabolic engineering efforts.

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Cofactor engineering

Cofactor engineering, a subset of metabolic engineering, is defined as the manipulation of the use of cofactors in an organism’s metabolic pathways. In cofactor engineering, the concentrations of cofactors are changed in order to maximize or minimize metabolic fluxes. This type of engineering can be used to optimize the production of a metabolite product or to increase the efficiency of a metabolic network. The use of engineering single celled organisms to create lucrative chemicals from cheap raw materials is growing, and cofactor engineering can play a crucial role in maximizing production. The field has gained more popularity in the past decade and has several practical applications in chemical manufacturing, bioengineering and pharmaceutical industries.

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Metabolite damage can occur through enzyme promiscuity or spontaneous chemical reactions. Many metabolites are chemically reactive and unstable and can react with other cell components or undergo unwanted modifications. Enzymatically or chemically damaged metabolites are always useless and often toxic. To prevent toxicity that can occur from the accumulation of damaged metabolites, organisms have damage-control systems that:

  1. Reconvert damaged metabolites to their original, undamaged form
  2. Convert a potentially harmful metabolite to a benign one
  3. Prevent damage from happening by limiting the build-up of reactive, but non-damaged metabolites that can lead to harmful products

References

  1. Yang, Y.T., Bennet, G. N., San, K.Y., (1998) Genetic and Metabolic Engineering, Electronic Journal of Biotechnology, ISSN   0717-3458
  2. N. Milne, P. Thomsen, N. Mølgaard Knudsen, P. Rubaszka, M. Kristensen, L. Borodina (2020-07-01). "Metabolic engineering of Saccharomyces cerevisiae for the de novo production of psilocybin and related tryptamine derivatives". Metabolic Engineering. 60: 25–36. doi: 10.1016/j.ymben.2019.12.007 . ISSN   1096-7176. PMC   7232020 .CS1 maint: multiple names: authors list (link)
  3. Kulkarni R, 2016. Metabolic Engineering: Biological Art of Producing Useful Chemicals. Resonance, 21 (3), 233-237.
  4. Vemuri, G.M, Aristidou, A.A, (2005) Metabolic Engineering in the -omics Era: Elucidating and Modulating Regulatory Networks, Microbial Mol Biology Review vol. 69: 197-216
  5. 1 2 Voit,Eberhard.,Torres,Nestor V.(2002)." Pathways Analysis and Optimization in Metabolic Engineering." Cambridge:University Press,p.ix-x
  6. Stephanopoulos, G. N., Aristidou, A. A., Nielsen, J. (1998). " Metabolic Engineering: Principles and Methodologies ". San Diego: Academic Press
  7. Dellomonaco, Clementina.(2011). Engineered Reversal of the beta oxidation cycle for the Synthesis of Fuels and Chemicals. Nature 476,355-359
  8. Patnaik, R. and Liao, J. (1994). "Engineering of Escherichia coli central metabolism for aromatic metabolite production with near theoretical yield". Appl. Environ. Microbiol. 60(11):3903-3908
  9. Keasling D.,Jay(2010). Advanced Biofuel production in microbes. Biotechnol.J.,5,147-162
  10. Martin, Vincent J. J.; Pitera, Douglas J.; Withers, Sydnor T.; Newman, Jack D.; Keasling, Jay D. (2003-07-01). "Engineering a mevalonate pathway in Escherichia coli for production of terpenoids". Nature Biotechnology. 21 (7): 796–802. doi:10.1038/nbt833. ISSN   1087-0156. PMID   12778056.
  11. Withers, Sydnor T.; Gottlieb, Shayin S.; Lieu, Bonny; Newman, Jack D.; Keasling, Jay D. (2007-10-01). "Identification of isopentenol biosynthetic genes from Bacillus subtilis by a screening method based on isoprenoid precursor toxicity". Applied and Environmental Microbiology. 73 (19): 6277–6283. doi:10.1128/AEM.00861-07. ISSN   0099-2240. PMC   2075014 . PMID   17693564.
  12. Stephanopoulos, Gregory (2012-11-16). "Synthetic Biology and Metabolic Engineering". ACS Synthetic Biology. 1 (11): 514–525. doi:10.1021/sb300094q. PMID   23656228.
  13. University of California - Los Angeles (2008, December 18). "Genetic Modification Turns E. Coli Bacteria Into High Density Biofuel". ScienceDaily. Retrieved December 7, 2011, from https://www.sciencedaily.com/releases/2008/12/081218151652.htm
  14. Schellenberger, J., Que, R., Fleming, R., et al. (2011). "Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0". Nature Protocols. 6(9):1290-1307
  15. Rocha, I., Maia, P., Evangelista, P., et al. (2010). "OptFlux: an open-source software platform for in silico metabolic engineering". BMC Sys Biol. 45(4)
  16. Work, T.S., Hinton, R., Work, E., Dobrota, M., Chard, T. (1980). "Laboratory Techniques in Biochemistry and Molecular Biology". v.8
  17. Wiechert, W. and de Graaf, A.A. (2000). "Bidirectional Reaction Steps in Metabolic Networks: Modeling and Simulation of Carbon Isotope Labeling Experiments". Biotechnol. Bioeng. 55(1):101-117

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