Metabolic Engineering Tools Research Articles

Organelle size control systems: From cell geometry to organelle‐directed medicine

Eukaryotic organelles encapsulate defined subsets of cellular biochemical pathways. For example, beta oxidation of fatty acids occurs inside mitochondria while fatty acid chain elongation takes place on the endoplasmic reticulum membrane. Organelle membranes isolate reactions from each other and store intermediates and products, and can thus be viewed as “reaction vessels”, playing roles analogous to the reflux columns and holding tanks of a chemical factory. To develop an effective chemical manufacturing process, it is not enough to focus just on the chemistry, i.e. the reactants and solvents that directly participate in reactions. The size and design of the reaction vessels is of equal importance. Likewise, within a cell, the size of organelles will influence the rates of biochemical pathways contained within them. Organelle surface area can limit the rate of import of substrates and efflux of products, while the volume of the organelle can dictate the quantity of intermediates that can build up (Figure 1). Many key metabolic enzymes are organelle membrane proteins, and in such cases increased surface area could allow larger numbers of molecules into the membrane to increase metabolic flux. Figure 1 Organelles as reaction vessels. Substrate in cytoplasm (Sc) is imported into an organelle through its bounding membrane to provide substrate inside the organelle (So). This organellar substrate is then subjected to several enzymatic steps in the organelle ... The influence of organelle size on metabolism is indicated by the fact that in cells specialized for certain pathways, the organelles that contain these pathways are enlarged compared to other cell types. Secretory cells are an obvious example, in which the requirement for a high rate of flux of secreted proteins is met by a massive over proliferation of endoplasmic reticulum and Golgi apparatus. Other examples include enlarged lipid droplets in adipose cells, proliferation of microvilli on the surface of cells lining the intestine, increased surface area and volume of rhodopsin containing vesicles in rods versus cones, and changes in mitochondrial abundance as a function of respiratory state. If, as we hypothesize, organelle size affects metabolism and signaling, then reprogramming of organelle size could be used as a novel strategy for reprogramming cellular state and behavior, with direct applications in medicine and biotechnology. Organelle-directed medicine and biotechnology Cytopathologists diagnose cancer by visual assessment of cell geometry including organelle size. For example, enlarged nuclei in a pap test indicates early stage cervical cancer. Cytopathology texts are full of such examples, but we don’t understand why these changes occur. According to the hypothesis of this review, these changes of cell geometry in cancer arise because cells have adapted to the metabolic alterations that are a hallmark of cancer [1]. Could we attack cancer cells by reprogramming organelle size? We can distinguish two possible reasons for organelle size alteration in cancer cells, which in turn predict two possible ways that organelle targeted therapy could be useful (Figure 2). First, if organelle size is adjusted as a response to pathological alterations in cell metabolism, then if we could reprogram organelle size in a cancer cell using small molecules that target the size control pathway, the cell might die due to a mismatch between organelle size and metabolic state. Alternatively, organelle size alterations might arise from pathological alterations in signaling pathways that impinge on the size control system, and then alterations in cell metabolism or behavior would be a downstream effect of the change to organelle size. In this case, it might be possible to drive the cell back to a less malignant state by driving its organelles towards a more normal size range. Either outcome would be therapeutically useful, but so far this “organelle directed medicine” strategy has not to our knowledge been tested in any cancer model system. Figure 2 Organelle size changes in disease: two strategies for organelle directed medicine. Disease causing mutations (for example, loss of tumor suppressor genes or activation of oncogenes) cause organelle size changes that are observed by the cytopathologist. ... Reprogramming organelle size would also have applications in metabolic engineering. Increasing the size of organelles that encapsulate key steps of metabolite production, especially those involving toxic intermediates, could greatly enhance metabolite production. For example biodiesel production could be enhanced by targeting genes that control lipid droplet size [2–3] thereby enhancing the ability of the cell to store triglyceride (TG). Before we can implement or test these applications in medicine and biotechnology, we need to obtain mechanistic understanding of how organelle size is regulated.

Metabolic engineering of Rhizopus oryzae for the production of platform chemicals

Rhizopus oryzae is a filamentous fungus belonging to the Zygomycetes. It is among others known for its ability to produce the sustainable platform chemicals l-(+)-lactic acid, fumaric acid, and ethanol. During glycolysis, all fermentable carbon sources are metabolized to pyruvate and subsequently distributed over the pathways leading to the formation of these products. These platform chemicals are produced in high yields on a wide range of carbon sources. The yields are in excess of 85 % of the theoretical yield for l-(+)-lactic acid and ethanol and over 65 % for fumaric acid. The study and optimization of the metabolic pathways involved in the production of these compounds requires well-developed metabolic engineering tools and knowledge of the genetic makeup of this organism. This review focuses on the current metabolic engineering techniques available for R. oryzae and their application on the metabolic pathways of the main fermentation products.

Stoichiometry based steady-state hepatic flux analysis: computational and experimental aspects.

: The liver has many complex physiological functions, including lipid, protein and carbohydrate metabolism, as well as bile and urea production. It detoxifies toxic substances and medicinal products. It also plays a key role in the onset and maintenance of abnormal metabolic patterns associated with various disease states, such as burns, infections and major traumas. Liver cells have been commonly used in in vitro experiments to elucidate the toxic effects of drugs and metabolic changes caused by aberrant metabolic conditions, and to improve the functions of existing systems, such as bioartificial liver. More recently, isolated liver perfusion systems have been increasingly used to characterize intrinsic metabolic changes in the liver caused by various perturbations, including systemic injury, hepatotoxin exposure and warm ischemia. Metabolic engineering tools have been widely applied to these systems to identify metabolic flux distributions using metabolic flux analysis or flux balance analysis and to characterize the topology of the networks using metabolic pathway analysis. In this context, hepatic metabolic models, together with experimental methodologies where hepatocytes or perfused livers are mainly investigated, are described in detail in this review. The challenges and opportunities are also discussed extensively.

Computational tools for metabolic engineering

A great variety of software applications are now employed in the metabolic engineering field. These applications have been created to support a wide range of experimental and analysis techniques. Computational tools are utilized throughout the metabolic engineering workflow to extract and interpret relevant information from large data sets, to present complex models in a more manageable form, and to propose efficient network design strategies. In this review, we present a number of tools that can assist in modifying and understanding cellular metabolic networks. The review covers seven areas of relevance to metabolic engineers. These include metabolic reconstruction efforts, network visualization, nucleic acid and protein engineering, metabolic flux analysis, pathway prospecting, post-structural network analysis and culture optimization. The list of available tools is extensive and we can only highlight a small, representative portion of the tools from each area.

Heterologous C-terminal signals effectively target fluorescent fusion proteins to leaf peroxisomes in diverse plant species

Peroxisomes are functionally diverse organelles that are wholly dependent on import of nuclear-encoded proteins. The signals that direct proteins into these organelles are either found at the C-terminus (type 1 peroxisomal targeting signal; PTS1) or N-terminus (type 2 peroxisomal targeting signal; PTS2) of the protein. Based on a limited number of tests in heterologous systems, PTS1 signals appear to be conserved across species. To further test the generality of this conclusion and to establish the extent to which the PTS1 signals can be relied on for biotechnological purposes across species, we tested two PTS1 signals for their ability to target fluorescent proteins in diverse plant species. Transient assays following microprojectile bombardment showed that the six amino acid PTS1 sequence (RAVARL) from spinach glycolate oxidase effectively targets green fluorescent fusion protein to the leaf peroxisomes in all 20 crops tested, including four monocots (sugarcane, wheat, corn and onion) and 16 dicots (carrot, cucumber, broccoli, tomato, lettuce, turnip, radish, cauliflower, cabbage, capsicum, celery, tobacco, petunia, beetroot, eggplant and coriander). Similarly, results indicated that the 10 amino acid PTS1 sequence (IHHPRELSRL) from pumpkin malate synthase effectively targets red fluorescent fusion protein to the leaf peroxisomes in all four crops tested including monocot (sugarcane) and dicot (cabbage, celery and pumpkin) species. These signal sequences should be useful metabolic engineering tools to direct recombinant proteins to the leaf peroxisomes in diverse plant species of biotechnological interest.

ChemInform Abstract: Platensimycin and Platencin: Promising Antibiotics for Future Application in Human Medicine

AbstractReview: 38 refs.

Synthetic biology and the development of tools for metabolic engineering

Synthetic biology can significantly advance metabolic engineering by contributing tools (minimal hosts, vectors, genetic controllers, characterized enzymes). The development of these tools significantly reduced the costs and time to develop the antimalarial drug artemisinin, but the availability of more tools could have reduced these costs substantially.

Engineering microbial chemical factories to produce renewable "biomonomers".

By applying metabolic engineering tools and strategies to engineer synthetic enzyme pathways, the number and diversity of commodity and specialty chemicals that can be derived directly from renewable feedstocks is rapidly and continually expanding. This of course includes a number of monomer building-block chemicals that can be used to produce replacements to many conventional plastic materials. This review aims to highlight numerous recent and important advancements in the microbial production of these so-called “biomonomers.” Relative to naturally-occurring renewable bioplastics, biomonomers offer several important advantages, including improved control over the final polymer structure and purity, the ability to synthesize non-natural copolymers, and allowing products to be excreted from cells which ultimately streamlines downstream recovery and purification. To highlight these features, a handful of biomonomers have been selected as illustrative examples of recent works, including polyamide monomers, styrenic vinyls, hydroxyacids, and diols. Where appropriate, examples of their industrial penetration to date and end-product uses are also highlighted. Novel biomonomers such as these are ultimately paving the way toward new classes of renewable bioplastics that possess a broader diversity of properties than ever before possible.

Phylogenomic study of lipid genes involved in microalgal biofuel production-candidate gene mining and metabolic pathway analyses.

Optimizing microalgal biofuel production using metabolic engineering tools requires an in-depth understanding of the structure-function relationship of genes involved in lipid biosynthetic pathway. In the present study, genome-wide identification and characterization of 398 putative genes involved in lipid biosynthesis in Arabidopsis thaliana Chlamydomonas reinhardtii, Volvox carteri, Ostreococcus lucimarinus, Ostreococcus tauri and Cyanidioschyzon merolae was undertaken on the basis of their conserved motif/domain organization and phylogenetic profile. The results indicated that the core lipid metabolic pathways in all the species are carried out by a comparable number of orthologous proteins. Although the fundamental gene organizations were observed to be invariantly conserved between microalgae and Arabidopsis genome, with increased order of genome complexity there seems to be an association with more number of genes involved in triacylglycerol (TAG) biosynthesis and catabolism. Further, phylogenomic analysis of the genes provided insights into the molecular evolution of lipid biosynthetic pathway in microalgae and confirm the close evolutionary proximity between the Streptophyte and Chlorophyte lineages. Together, these studies will improve our understanding of the global lipid metabolic pathway and contribute to the engineering of regulatory networks of algal strains for higher accumulation of oil.

Metabolic network modeling of redox balancing and biohydrogen production in purple nonsulfur bacteria

BackgroundPurple nonsulfur bacteria (PNSB) are facultative photosynthetic bacteria and exhibit an extremely versatile metabolism. A central focus of research on PNSB dealt with the elucidation of mechanisms by which they manage to balance cellular redox under diverse conditions, in particular under photoheterotrophic growth.ResultsGiven the complexity of the central metabolism of PNSB, metabolic modeling becomes crucial for an integrated analysis of the accumulated biological knowledge. We reconstructed a stoichiometric model capturing the central metabolism of three important representatives of PNSB (Rhodospirillum rubrum, Rhodobacter sphaeroides and Rhodopseudomonas palustris). Using flux variability analysis, the model reveals key metabolic constraints related to redox homeostasis in these bacteria. With the help of the model we can (i) give quantitative explanations for non-intuitive, partially species-specific phenomena of photoheterotrophic growth of PNSB, (ii) reproduce various quantitative experimental data, and (iii) formulate several new hypotheses. For example, model analysis of photoheterotrophic growth reveals that - despite a large number of utilizable catabolic pathways - substrate-specific biomass and CO2 yields are fixed constraints, irrespective of the assumption of optimal growth. Furthermore, our model explains quantitatively why a CO2 fixing pathway such as the Calvin cycle is required by PNSB for many substrates (even if CO2 is released). We also analyze the role of other pathways potentially involved in redox metabolism and how they affect quantitatively the required capacity of the Calvin cycle. Our model also enables us to discriminate between different acetate assimilation pathways that were proposed recently for R. sphaeroides and R. rubrum, both lacking the isocitrate lyase. Finally, we demonstrate the value of the metabolic model also for potential biotechnological applications: we examine the theoretical capabilities of PNSB for photoheterotrophic hydrogen production and identify suitable genetic interventions to increase the hydrogen yield.ConclusionsTaken together, the metabolic model (i) explains various redox-related phenomena of the versatile metabolism of PNSB, (ii) delivers new hypotheses on the operation and relevance of several metabolic pathways, and (iii) holds significant potential as a tool for rational metabolic engineering of PNSB in biotechnological applications.

Platensimycin and platencin: promising antibiotics for future application in human medicine

Platensimycin and platencin are novel antibiotics produced by Streptomyces platensis. They are potent and non-toxic natural products active against Gram-positive pathogens, including antibiotic-resistant strains and Mycobacterium tuberculosis. They were isolated using an intriguing target-based whole-cell antisense differential sensitivity assay as inhibitors of fatty acid biosynthesis of type II. This type of biosynthesis is not present in humans. Platensimycin inhibits the elongation-condensing enzyme FabF, whereas platencin inhibits both FabF and FabH. For these antibiotics to become successful drugs, their pharmacokinetics must be improved. They have too high a rate of clearance in the body, yielding a low degree of systematic exposure. They work well when administered by continuous infusion, but this is not a useful method of delivery to patients. The two antibiotics and many analogs have been prepared by chemical synthesis. Natural congeners have also been obtained from the producing actinomycete. However, none of these molecules are as active as platensimycin and platencin. Using tools of rational metabolic engineering, superior strains have been produced making hundreds of times more antibiotic than the natural strains.

Metabolic engineering in dark fermentative hydrogen production; theory and practice

Dark fermentation is an attractive option for hydrogen production since it could use already existing reactor technology and readily available substrates without requiring a direct input of solar energy. However, a number of improvements are required before the rates and yields of such a process approach those required for a practical process. Among the options for achieving the required advances, metabolic engineering offers some powerful tools for remodeling microbes to increase product production rates and molar yields. Here we review the current metabolic engineering tool box that is available, discuss the current status of engineering efforts as applied to dark hydrogen production, and suggest areas for future improvements.

Advanced Stoichiometric Analysis of Metabolic Networks of Mammalian Systems

Metabolic engineering tools have been widely applied to living organisms to gain a comprehensive understanding about cellular networks and to improve cellular properties. Metabolic flux analysis (MFA), flux balance analysis (FBA), and metabolic pathway analysis (MPA) are among the most popular tools in stoichiometric network analysis. Although application of these tools into well-known microbial systems is extensive in the literature, various barriers prevent them from being utilized in mammalian cells. Limited experimental data, complex regulatory mechanisms, and the requirement of more complex nutrient media are some major obstacles in mammalian cell systems. However, mammalian cells have been used to produce therapeutic proteins, to characterize disease states or related abnormal metabolic conditions, and to analyze the toxicological effects of some medicinally important drugs. Therefore, there is a growing need for extending metabolic engineering principles to mammalian cells in order to understand their underlying metabolic functions. In this review article, advanced metabolic engineering tools developed for stoichiometric analysis including MFA, FBA, and MPA are described. Applications of these tools in mammalian cells are discussed in detail, and the challenges and opportunities are highlighted.

From Pathways to Genomes and Beyond: The Metabolic Engineering Toolbox and Its Place in Biofuels Production

AbstractConcerns about the availability of petroleum-derived fuels and chemicals have led to the exploration of metabolically engineered organisms as novel hosts for biofuels and chemicals production. However, the complexity inherent in metabolic and regulatory networks makes this undertaking a complex task. To address these limitations, metabolic engineering has adapted a wide-variety of tools for altering phenotypes. In this review, we will highlight traditional and recent metabolic engineering tools for optimizing cells including pathway-based, global, and genomics enabled approaches. Specifically, we describe these tools as well as provide demonstrations of their effectiveness in optimizing biofuels production. However, each of these tools provides stepping stones towards the grand goal of biofuels production. Thus, developing methods for largescale cellular optimization and integrative approaches are invaluable for further cell optimization. This review highlights the challenges that still must be met to accomplish this goal.

Optimizing pentose utilization in yeast: the need for novel tools and approaches

Hexose and pentose cofermentation is regarded as one of the chief obstacles impeding economical conversion of lignocellulosic biomass to biofuels. Over time, successful application of traditional metabolic engineering strategy has produced yeast strains capable of utilizing the pentose sugars (especially xylose and arabinose) as sole carbon sources, yet major difficulties still remain for engineering simultaneous, exogenous sugar metabolism. Beyond catabolic pathways, the focus must shift towards non-traditional aspects of cellular engineering such as host molecular transport capability, catabolite sensing and stress response mechanisms. This review highlights the need for an approach termed 'panmetabolic engineering', a new paradigm for integrating new carbon sources into host metabolic pathways. This approach will concurrently optimize the interdependent processes of transport and metabolism using novel combinatorial techniques and global cellular engineering. As a result, panmetabolic engineering is a whole pathway approach emphasizing better pathways, reduced glucose-induced repression and increased product tolerance. In this paper, recent publications are reviewed in light of this approach and their potential to expand metabolic engineering tools. Collectively, traditional approaches and panmetabolic engineering enable the reprogramming of extant biological complexity and incorporation of exogenous carbon catabolism.

Engineered polyketide biosynthesis and biocatalysis in Escherichia coli

Polyketides are important bioactive natural products biosynthesized by bacteria, fungi, and plants. The enzymes that synthesize polyketides are collectively referred to as polyketide synthases (PKSs). Because many of the natural hosts that produce polyketides are difficult to culture or manipulate, establishing a universal heterologous host that is genetically tractable has become an important goal toward the engineered biosynthesis of polyketides and analogues. Here, we summarize the recent progresses in engineering Escherichia coli as a heterologous host for reconstituting PKSs of different types. Our increased understanding of PKS enzymology and structural biology, combined with new tools in protein engineering, metabolic engineering, and synthetic biology, has firmly established E. coli as a powerful host for producing polyketides.

Biofuel production improvement with genome‐scale models: The role of cell composition

Genome-scale models have developed into a vital tool for rational metabolic engineering. These models balance cofactors and energetic requirements and determine biosynthetic precursor availability in response to environmental and genetic perturbations. In particular, allocation of additional reducing power is an important strategy for engineering potential biofuel production from microbes. Many potential biofuel solvents induce biomolecular changes on the host organism that are not yet captured by genome-scale models. Here, methods of construction for several biomass constituting equations are reviewed along with potential changes to cellular composition with potential biofuels exposure. The biomass constituting equations of potential host organisms with existing genome-scale models are compared side-by-side to explore their evolution over the years and to explore differences that arise when these equations are compiled by different research groups. Genome-scale model simulation results attempt to address and provide guidance for further research into: (i) whether inconsistencies in the biomass constituting equations are relevant to predictions of solvent production, (ii) what level of detail is necessary to accurately describe cellular composition, and (iii) future developments that may enable more accurate characterizations of biomolecular composition.

Systems metabolic engineering: genome-scale models and beyond.

The advent of high throughput genome-scale bioinformatics has led to an exponential increase in available cellular system data. Systems metabolic engineering attempts to use data-driven approaches--based on the data collected with high throughput technologies--to identify gene targets and optimize phenotypical properties on a systems level. Current systems metabolic engineering tools are limited for predicting and defining complex phenotypes such as chemical tolerances and other global, multigenic traits. The most pragmatic systems-based tool for metabolic engineering to arise is the in silico genome-scale metabolic reconstruction. This tool has seen wide adoption for modeling cell growth and predicting beneficial gene knockouts, and we examine here how this approach can be expanded for novel organisms. This review will highlight advances of the systems metabolic engineering approach with a focus on de novo development and use of genome-scale metabolic reconstructions for metabolic engineering applications. We will then discuss the challenges and prospects for this emerging field to enable model-based metabolic engineering. Specifically, we argue that current state-of-the-art systems metabolic engineering techniques represent a viable first step for improving product yield that still must be followed by combinatorial techniques or random strain mutagenesis to achieve optimal cellular systems.

Trichome specific expression of the tobacco (Nicotiana sylvestris) cembratrien-ol synthase genes is controlled by both activating and repressing cis-regions

Tobacco (Nicotiana sylvestris) glandular trichomes make an attractive target for isoprenoid metabolic engineering because they produce large amounts of one type of diterpenoids, alpha- and beta-cembratrien-diols. This article describes the establishment of tools for metabolic engineering of tobacco trichomes, namely a transgenic line with strongly reduced levels of diterpenoids in the exudate and the characterization of a trichome specific promoter. The diterpene-free tobacco line was generated by silencing the major tobacco diterpene synthases, which were found to be encoded by a family of four highly similar genes (NsCBTS-2a, NsCBTS-2b, NsCBTS-3 and NsCBTS-4), one of which is a pseudogene. The promoter regions of all four CBTS genes were sequenced and found to share over 95% identity between them. Transgenic plants expressing uidA under the control of the NsCBTS-2a promoter displayed a specific pattern of GUS expression restricted exclusively to the glandular cells of the tall secretory trichomes. A series of sequential and internal deletions of the NsCBTS-2a promoter led to the identification of two cis-acting regions. The first, located between positions -589 to -479 from the transcription initiation site, conferred a broad transcriptional activation, not only in the glandular cells, but also in cells of the trichome stalk, as well as in the leaf epidermis and the root. The second region, located between positions -279 to -119, had broad repressor activity except in trichome glandular cells and is mainly responsible for the specific expression pattern of the NsCBTS-2a gene. These results establish the basis for the identification of trans-regulators required for the expression of the CBTS genes restricted to the secretory cells of the glandular trichomes.

OptFlux: an open-source software platform for in silico metabolic engineering

BackgroundOver the last few years a number of methods have been proposed for the phenotype simulation of microorganisms under different environmental and genetic conditions. These have been used as the basis to support the discovery of successful genetic modifications of the microbial metabolism to address industrial goals. However, the use of these methods has been restricted to bioinformaticians or other expert researchers. The main aim of this work is, therefore, to provide a user-friendly computational tool for Metabolic Engineering applications.ResultsOptFlux is an open-source and modular software aimed at being the reference computational application in the field. It is the first tool to incorporate strain optimization tasks, i.e., the identification of Metabolic Engineering targets, using Evolutionary Algorithms/Simulated Annealing metaheuristics or the previously proposed OptKnock algorithm. It also allows the use of stoichiometric metabolic models for (i) phenotype simulation of both wild-type and mutant organisms, using the methods of Flux Balance Analysis, Minimization of Metabolic Adjustment or Regulatory on/off Minimization of Metabolic flux changes, (ii) Metabolic Flux Analysis, computing the admissible flux space given a set of measured fluxes, and (iii) pathway analysis through the calculation of Elementary Flux Modes.OptFlux also contemplates several methods for model simplification and other pre-processing operations aimed at reducing the search space for optimization algorithms.The software supports importing/exporting to several flat file formats and it is compatible with the SBML standard. OptFlux has a visualization module that allows the analysis of the model structure that is compatible with the layout information of Cell Designer, allowing the superimposition of simulation results with the model graph.ConclusionsThe OptFlux software is freely available, together with documentation and other resources, thus bridging the gap from research in strain optimization algorithms and the final users. It is a valuable platform for researchers in the field that have available a number of useful tools. Its open-source nature invites contributions by all those interested in making their methods available for the community.Given its plug-in based architecture it can be extended with new functionalities. Currently, several plug-ins are being developed, including network topology analysis tools and the integration with Boolean network based regulatory models.