Metabolic engineering

Metabolic engineering has developed as a very powerful approach to optimising industrial fermentation processes through the introduction of directed genetic changes using recombinant DNA technology. Successful metabolic engineering starts with a careful analysis of cellular function; based on the results of this analysis, an improved strain is designed and subsequently constructed by genetic engineering. In recent years some very powerful tools have been developed, both for analysing cellular function and for introducing directed genetic changes. In this paper, some of these tools are reviewed and many examples of metabolic engineering are presented to illustrate the power of the technology. The examples are categorised according to the approach taken or the aim: (1) heterologous protein production, (2) extension of substrate range, (3) pathways leading to new products, (4) pathways for degradation of xenobiotics, (5) improvement of overall cellular physiology, (6) elimination or reduction of by-product formation, and (7) improvement of yield or productivity.     

Metabolic engineering of beta-lactam production

Metabolic engineering has become a rational alternative to classical strain improvement in optimisation of beta-lactam production. In metabolic engineering directed genetic modification are introduced to improve the cellular properties of the production strains. This has resulted in substantial increases in the existing beta-lactam production processes. Furthermore, pathway extension, by heterologous expression of novel genes in well-characterised strains, has led to introduction of new fermentation processes that replace environmentally damaging chemical methods. This minireview discusses the recent developments in metabolic engineering and the applications of this approach for improving beta-lactam production.     

Automated engineering of synthetic metabolic pathways for efficient biomanufacturing

Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial production. Metabolic engineering is nowadays on track towards a truly manufacturing technology, with reduced times from conception to production enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising production routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an experimental design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufacturing process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomolecules in an affordable, inclusive and sustainable bioeconomy.     

Metabolic engineering under uncertainty. I: framework development

Standard bioprocess conditions have been widely applied for the microbial conversion of raw material to essential industrial products. Successful metabolic engineering (ME) strategies require a comprehensive framework to manage the complexity embedded in cellular metabolism, to explore the impacts of bioprocess conditions on the cellular responses, and to deal with the uncertainty of the physiochemical parameters. We have recently developed a computational and statistical framework that is based on Metabolic Control Analysis and uses a Monte Carlo method to simulate the uncertainty in the values of the system parameters [Wang, L., Birol, I., Hatzimanikatis, V., 2004. Metabolic control analysis under uncertainty: framework development and case studies. Biophys. J. 87(6), 3750-3763]. In this work, we generalize this framework to incorporate the central cellular processes, such as cell growth, and different bioprocess conditions, such as different types of bioreactors. The framework provides the mathematical basis for the quantification of the interactions between intracellular metabolism and extracellular conditions, and it is readily applicable to the identification of optimal ME targets for the improvement of industrial processes [Wang, L., Hatzimanikatis, V., 2005. Metabolic engineering under uncertainty. II: analysis of yeast metabolism. Submitted].     

Metabolic engineering of bacteria

Yield and productivity are critical for the economics and viability of a bioprocess. In metabolic engineering the main objective is the increase of a target metabolite production through genetic engineering. Metabolic engineering is the practice of optimizing genetic and regulatory processes within cells to increase the production of a certain substance. In the last years, the development of recombinant DNA technology and other related technologies has provided new tools for approaching yields improvement by means of genetic manipulation of biosynthetic pathway. Industrial microorganisms like Escherichia coli, Actinomycetes, etc. have been developed as biocatalysts to provide new or to optimize existing processes for the biotechnological production of chemicals from renewable plant biomass. The factors like oxygenation, temperature and pH have been traditionally controlled and optimized in industrial fermentation in order to enhance metabolite production. Metabolic engineering of bacteria shows a great scope in industrial application as well as such technique may also have good potential to solve certain metabolic disease and environmental problems in near future.     

Large-scale bi-level strain design approaches and mixed-integer programming solution techniques

The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ～10 days to ～5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution techniques developed here will facilitate the strain design process and extend the scope of its application to metabolic engineering.     

Engineering precursor flow for increased erythromycin production in Aeromicrobium erythreum

Metabolic engineering technology for industrial microorganisms is under development to create rational, more reliable, and more cost-effective approaches to strain improvement. Strain improvement is a critical component of the drug development process, yet the genetic basis for high production by industrial microorganisms is still a mystery. In this study, a search was begun for genetic modifications critical for high-level antibiotic production. The model system used was erythromycin production studied in the unicellular actinomycete, Aeromicrobium erythreum. A tagged-mutagenesis approach allowed reverse engineering of improved strains, revealing two genes, mutB and cobA, in the primary metabolic branch for methylmalonyl-CoA utilization. Knockouts in these genes created a permanent metabolic switch in the flow of methylmalonyl-CoA, from the primary branch into a secondary metabolic branch, driving erythromycin overproduction. The model provides insights into the regulation and evolution of secondary metabolism.     

Leveraging knowledge engineering and machine learning for microbial bio-manufacturing

Genome scale modeling (GSM) predicts the performance of microbial workhorses and helps identify beneficial gene targets. GSM integrated with intracellular flux dynamics, omics, and thermodynamics have shown remarkable progress in both elucidating complex cellular phenomena and computational strain design (CSD). Nonetheless, these models still show high uncertainty due to a poor understanding of innate pathway regulations, metabolic burdens, and other factors (such as stress tolerance and metabolite channeling). Besides, the engineered hosts may have genetic mutations or non-genetic variations in bioreactor conditions and thus CSD rarely foresees fermentation rate and titer. Metabolic models play important role in design-build-test-learn cycles for strain improvement, and machine learning (ML) may provide a viable complementary approach for driving strain design and deciphering cellular processes. In order to develop quality ML models, knowledge engineering leverages and standardizes the wealth of information in literature (e.g., genomic/phenomic data, synthetic biology strategies, and bioprocess variables). Data driven frameworks can offer new constraints for mechanistic models to describe cellular regulations, to design pathways, to search gene targets, and to estimate fermentation titer/rate/yield under specified growth conditions (e.g., mixing, nutrients, and O₂). This review highlights the scope of information collections, database constructions, and machine learning techniques (such as deep learning and transfer learning), which may facilitate “Learn and Design” for strain development.     

Metabolic engineering of Saccharomyces cerevisiae for the production of triacetic acid lactone

Biobased chemicals have become attractive replacements for their fossil-fuel counterparts. Recent studies have shown triacetic acid lactone (TAL) to be a promising candidate, capable of undergoing chemical conversion to sorbic acid and other valuable intermediates. In this study, Saccharomyces cerevisiae was engineered for the high-level production of TAL by overexpression of the Gerbera hybrida 2-pyrone synthase (2-PS) and systematic engineering of the yeast metabolic pathways. Pathway analysis and a computational approach were employed to target increases in cofactor and precursor pools to improve TAL synthesis. The pathways engineered include those for energy storage and generation, pentose biosynthesis, gluconeogenesis, lipid biosynthesis and regulation, cofactor transport, and fermentative capacity. Seventeen genes were selected for disruption and independently screened for their effect on TAL production; combinations of knockouts were then evaluated. A combination of the pathway engineering and optimal culture parameters led to a 37-fold increase in titer to 2.2 g/L and a 50-fold increase in yield to 0.13 (g/g glucose). These values are the highest reported in the literature, and provide a 3-fold improvement in yield over previous reports using S. cerevisiae. Identification of these metabolic bottlenecks provides a strategy for overproduction of other acetyl-CoA-dependent products in yeast.     

Systems metabolic engineering of microorganisms for natural and non-natural chemicals

Growing concerns over limited fossil resources and associated environmental problems are motivating the development of sustainable processes for the production of chemicals, fuels and materials from renewable resources. Metabolic engineering is a key enabling technology for transforming microorganisms into efficient cell factories for these compounds. Systems metabolic engineering, which incorporates the concepts and techniques of systems biology, synthetic biology and evolutionary engineering at the systems level, offers a conceptual and technological framework to speed the creation of new metabolic enzymes and pathways or the modification of existing pathways for the optimal production of desired products. Here we discuss the general strategies of systems metabolic engineering and examples of its application and offer insights as to when and how each of the different strategies should be used. Finally, we highlight the limitations and challenges to be overcome for the systems metabolic engineering of microorganisms at more advanced levels.     

Apomixis for crop improvement

Summary Apomixis is a genetically controlled reproductive process by which embryos and seeds develop in the ovule without female meiosis and egg cell fertilization. Apomixis produces seed progeny that are exact replicas of the mother plant. The major advantage of apomixis over sexual reproduction is the possibility to select individuals with desirable gene combinations and to propagate them as clones. In contrast to clonal propagation through somatic embryogenesis or in vitro shoot multiplication, apomixis avoids the need for costly processes, such as the production of artificial seeds and tissue culture. It simplifies the processes of commercial hybrid and cultivar production and enables a large-scale seed production economically in both seed- and vegetatively propagated crops. In vegetatively reproduced plants (e.g., potato), the main applications of apomixis are the avoidance of phytosanitary threats and the spanning of unfavorable seasons. Because of its potential for crop improvement and global agricultural production, apomixis is now receiving increasing attention from both scientific and industrial sectors. Harnessing apomixis is a major goal in applied plant genetic engineering. In this regard, efforts are focused on genetic and breeding strategies in various plant species, combined with molecular methods to analyze apomictic and sexual modes of reproduction and to identify key regulatory genes and mechanisms underlying these processes. Also, investigations on the components of apomixis, i.e., apomeiosis, parthenogenesis, and endosperm development without fertilization, genetic screens for apomictic mutants and transgenic approaches to modify sexual reproduction by using various regulatory genes are receiving a major effort. These can open new avenues for the transfer of the apomixis trait to important crop species and will have far-reaching potentials in crop improvement regarding agricultural production and the quality of the products.     

NIBBS-search for fast and accurate prediction of phenotype-biased metabolic systems

Understanding of genotype-phenotype associations is important not only for furthering our knowledge on internal cellular processes, but also essential for providing the foundation necessary for genetic engineering of microorganisms for industrial use (e.g., production of bioenergy or biofuels). However, genotype-phenotype associations alone do not provide enough information to alter an organism's genome to either suppress or exhibit a phenotype. It is important to look at the phenotype-related genes in the context of the genome-scale network to understand how the genes interact with other genes in the organism. Identification of metabolic subsystems involved in the expression of the phenotype is one way of placing the phenotype-related genes in the context of the entire network. A metabolic system refers to a metabolic network subgraph; nodes are compounds and edges labels are the enzymes that catalyze the reaction. The metabolic subsystem could be part of a single metabolic pathway or span parts of multiple pathways. Arguably, comparative genome-scale metabolic network analysis is a promising strategy to identify these phenotype-related metabolic subsystems. Network Instance-Based Biased Subgraph Search (NIBBS) is a graph-theoretic method for genome-scale metabolic network comparative analysis that can identify metabolic systems that are statistically biased toward phenotype-expressing organismal networks. We set up experiments with target phenotypes like hydrogen production, TCA expression, and acid-tolerance. We show via extensive literature search that some of the resulting metabolic subsystems are indeed phenotype-related and formulate hypotheses for other systems in terms of their role in phenotype expression. NIBBS is also orders of magnitude faster than MULE, one of the most efficient maximal frequent subgraph mining algorithms that could be adjusted for this problem. Also, the set of phenotype-biased metabolic systems output by NIBBS comes very close to the set of phenotype-biased subgraphs output by an exact maximally-biased subgraph enumeration algorithm ( MBS-Enum ). The code (NIBBS and the module to visualize the identified subsystems) is available at http://freescience.org/cs/NIBBS.     

Evolutionary engineering of Saccharomyces cerevisiae for improved industrially important properties

This article reviews evolutionary engineering of Saccharomyces cerevisiae. Following a brief introduction to the 'rational' metabolic engineering approach and its limitations such as extensive genetic and metabolic information requirement on the organism of interest, complexity of cellular physiological responses, and difficulties of cloning in industrial strains, evolutionary engineering is discussed as an alternative, inverse metabolic engineering strategy. Major evolutionary engineering applications with S. cerevisiae are then discussed in two general categories: (1) evolutionary engineering of substrate utilization and product formation and (2) evolutionary engineering of stress resistance. Recent developments in functional genomics methods allow rapid identification of the molecular basis of the desired phenotypes obtained by evolutionary engineering. To conclude, when used alone or in combination with rational metabolic engineering and/or computational methods to study and analyze processes of adaptive evolution, evolutionary engineering is a powerful strategy for improvement in industrially important, complex properties of S. cerevisiae.     

Artificial promoters for metabolic optimization

In this article, we review some of the expression systems that are available for Metabolic Control Analysis and Metabolic Engineering, and examine their advantages and disadvantages in different contexts. In a recent approach, artificial promoters for modulating gene expression in micro-organisms were constructed using synthetic degenerated oligonucleotides. From this work, a promoter library was obtained for Lactococcus lactis, containing numerous individual promoters and covering a wide range of promoter activities. Importantly, the range of promoter activities was covered in small steps of activity change. Promoter libraries generated by this approach allow for optimization of gene expression and for experimental control analysis in a wide range of biological systems by choosing from the promoter library promoters giving, e.g., 25%, 50%, 200%, and 400% of the normal expression level of the gene in question. If the relevant variable (e.g., the flux or yield) is then measured with each of these constructs, then one can calculate the control coefficient and determine the optimal expression level. One advantage of the method is that the construct which is found to have the optimal expression level is then, in principle, ready for use in the industrial fermentation process; another advantage is that the system can be used to optimize the expression of different enzymes within the same cell.     

Microbial production of polyhydroxybutyrate with tailor-made properties: an integrated modelling approach and experimental validation

The microbial production of polyhydroxybutyrate (PHB) is a complex process in which the final quantity and quality of the PHB depend on a large number of process operating variables. Consequently, the design and optimal dynamic operation of a microbial process for the efficient production of PHB with tailor-made molecular properties is an extremely interesting problem. The present study investigates how key process operating variables (i.e., nutritional and aeration conditions) affect the biomass production rate and the PHB accumulation in the cells and its associated molecular weight distribution. A combined metabolic/polymerization/macroscopic modelling approach, relating the process performance and product quality with the process variables, was developed and validated using an extensive series of experiments and measurements. The model predicts the dynamic evolution of the biomass growth, the polymer accumulation, the consumption of carbon and nitrogen sources and the average molecular weights of the PHB in a bioreactor, under batch and fed-batch operating conditions. The proposed integrated model was used for the model-based optimization of the production of PHB with tailor-made molecular properties in Azohydromonas lata bacteria. The process optimization led to a high intracellular PHB accumulation (up to 95% g of PHB per g of DCW) and the production of different grades (i.e., different molecular weight distributions) of PHB.     

代谢工程

代谢工程,也称途径工程,是基因工程一个重要分支,一般是多基因的基因工程,与细胞的基因调控、代谢调控和生化工程密切相关。讨论了代谢工程的应用,包括通过改变代谢流和代谢途径提高产量,改善生产过程,构建新的代谢途径和产生新的代谢产物等。     

Metabolic flux engineering of L-lysine production in Corynebacterium glutamicum--over expression and modification of G6P dehydrogenase

In the present work, metabolic flux engineering of Corynebacterium glutamicum was carried out to increase lysine production. The strategy focused on engineering of the pentose phosphate pathway (PPP) flux by different genetic modifications. Over expression of the zwf gene, encoding G6P dehydrogenase, in the feedback-deregulated lysine-producing strain C. glutamicum ATCC 13032 lysC(fbr) resulted in increased lysine production on different carbon sources including the two major industrial sugars, glucose and sucrose. The additional introduction of the A243T mutation into the zwf gene and the over expression of fructose 1,6-bisphosphatase resulted in a further successive improvement of lysine production. Hereby the point mutation resulted in higher affinity of G6P dehydrogenase towards NADP and reduced sensitivity against inhibition by ATP, PEP and FBP. Overall, the lysine yield increased up to 70% through the combination of the different genetic modifications. Through strain engineering formation of trehalose was reduced by up to 70% due to reduced availability of its precursor G6P. Metabolic flux analysis revealed a 15% increase of PPP flux in response to over expression of the zwf gene. Overall a strong apparent NADPH excess resulted. Redox balancing indicated that this excess is completely oxidized by malic enzyme.     

Quantitative characterization of metabolism and metabolic shifts during growth of the new human cell line AGE1.HN using time resolved metabolic flux analysis

For the improved production of vaccines and therapeutic proteins, a detailed understanding of the metabolic dynamics during batch or fed-batch production is requested. To study the new human cell line AGE1.HN, a flexible metabolic flux analysis method was developed that is considering dynamic changes in growth and metabolism during cultivation. This method comprises analysis of formation of cellular components as well as conversion of major substrates and products, spline fitting of dynamic data and flux estimation using metabolite balancing. During batch cultivation of AGE1.HN three distinct phases were observed, an initial one with consumption of pyruvate and high glycolytic activity, a second characterized by a highly efficient metabolism with very little energy spilling waste production and a third with glutamine limitation and decreasing viability. Main events triggering changes in cellular metabolism were depletion of pyruvate and glutamine. Potential targets for the improvement identified from the analysis are (i) reduction of overflow metabolism in the beginning of cultivation, e.g. accomplished by reduction of pyruvate content in the medium and (ii) prolongation of phase 2 with its highly efficient energy metabolism applying e.g. specific feeding strategies. The method presented allows fast and reliable metabolic flux analysis during the development of producer cells and production processes from microtiter plate to large scale reactors with moderate analytical and computational effort. It seems well suited to guide media optimization and genetic engineering of producing cell lines.     

Use of genome-scale microbial models for metabolic engineering

Metabolic engineering serves as an integrated approach to design new cell factories by providing rational design procedures and valuable mathematical and experimental tools. Mathematical models have an important role for phenotypic analysis, but can also be used for the design of optimal metabolic network structures. The major challenge for metabolic engineering in the post-genomic era is to broaden its design methodologies to incorporate genome-scale biological data. Genome-scale stoichiometric models of microorganisms represent a first step in this direction.