Computational identification of gene over-expression targets for metabolic engineering of taxadiene production

In silico target-based strain engineering of Saccharomyces cerevisiae for terpene precursor improvement

Systems-based metabolic engineering enables cells to enhance product formation by predicting gene knockout and overexpression targets using modeling tools. FOCuS, a novel metaheuristic tool, was used to predict flux improvement targets in terpenoid pathway using the genome-scale model of Saccharomyces cerevisiae, iMM904. Some of the key knockout target predicted includes LYS1, GAP1, AAT1, AAT2, TH17, KGD-m, MET14, PDC1 and ACO1. It was also observed that the knockout reactions belonged either to fatty acid biosynthesis, amino acid synthesis pathways or nucleotide biosynthesis pathways. Similarly, overexpression targets such as PFK1, FBA1, ZWF1, TDH1, PYC1, ALD6, TPI1, PDX1 and ENO1 were established using three different existing gene amplification algorithms. Most of the overexpression targets belonged to glycolytic and pentose phosphate pathways. Each of these targets had plausible role for improving flux toward sterol pathway and were seemingly not artifacts. Moreover, an in vitro study as validation was carried with overexpression of ALD6 and TPI1. It was found that there was an increase in squalene synthesis by 2.23- and 4.24- folds, respectively, when compared with control. In general, the rationale for predicting these in silico targets was attributed to either increasing the acetyl-CoA precursor pool or regeneration of NADPH, which increase the sterol pathway flux.

A Computational Framework to Identify Metabolic Engineering Strategies for the Co-Production of Metabolites

Microbial production of chemicals is a more sustainable alternative to traditional chemical processes. However, the shift to bioprocess is usually accompanied by a drop in economic feasibility. Co-production of more than one chemical can improve the economy of bioprocesses, enhance carbon utilization and also ensure better exploitation of resources. While a number of tools exist for in silico metabolic engineering, there is a dearth of computational tools that can co-optimize the production of multiple metabolites. In this work, we propose co-FSEOF (co-production using Flux Scanning based on Enforced Objective Flux), an algorithm designed to identify intervention strategies to co-optimize the production of a set of metabolites. Co-FSEOF can be used to identify all pairs of products that can be co-optimized with ease using a single intervention. Beyond this, it can also identify higher-order intervention strategies for a given set of metabolites. We have employed this tool on the genome-scale metabolic models of Escherichia coli and Saccharomyces cerevisiae, and identified intervention targets that can co-optimize the production of pairs of metabolites under both aerobic and anaerobic conditions. Anaerobic conditions were found to support the co-production of a higher number of metabolites when compared to aerobic conditions in both organisms. The proposed computational framework will enhance the ease of study of metabolite co-production and thereby aid the design of better bioprocesses.

Metabolic engineering of Bacillus subtilis based on genome-scale metabolic model to promote fengycin production

Fengycin is an important lipopeptide antibiotic that can be produced by Bacillus subtilis. However, the production capacity of the unmodified wild strain is very low. Therefore, a computationally guided engineering method was proposed to improve the fengycin production capacity. First, based on the annotated genome and biochemical information, a genome-scale metabolic model of Bacillus subtilis 168 was constructed. Subsequently, several potential genetic targets were identified through the flux balance analysis and minimization of metabolic adjustment algorithm that can ensure an increase in the production of fengycin. In addition, according to the results predicted by the model, the target genes accA (encoding acetyl-CoA carboxylase), cypC (encoding fatty acid beta-hydroxylating cytochrome P450) and gapA (encoding glyceraldehyde-3-phosphate dehydrogenase) were overexpressed in the parent strain Bacillus subtilis 168. The yield of fengycin was increased by 56.4, 46.6, and 20.5% by means of the overexpression of accA, cypC, and gapA, respectively, compared with the yield from the parent strain. The relationship between the model prediction and experimental results proves the effectiveness and rationality of this method for target recognition and improving fengycin production.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-021-02990-7.

Computational and synthetic biology approaches for the biosynthesis of antiviral and anticancer terpenoids from Bacillus subtilis

Recent advancements in medicinal research have identified several antiviral and anticancer terpenoids that are usually deployed as a source of flavor, fragrances and pharmaceuticals. Under the current COVID-19 pandemic conditions, natural therapeutics with least side effects are the need of the hour to save the patients, especially, which are pre-affected with other medical complications. Although, plants are the major sources of terpenoids; however, for the environmental concerns, the global interest has shifted to the biocatalytic production of molecules from microbial sources. The gram-positive bacterium Bacillus subtilis is a suitable host in this regard due to its GRAS (generally regarded as safe) status, ease in genetic manipulations and wide industrial acceptability. The B. subtilis synthesizes its terpenoid molecules from 1-deoxy-d-xylulose-5-phosphate (DXP) pathway, a common route in almost all microbial strains. Here, we summarize the computational and synthetic biology approaches to improve the production of terpenoid-based therapeutics from B. subtilis by utilizing DXP pathway. We focus on the in-silico approaches for screening the functionally improved enzyme-variants of the two crucial enzymes namely, the DXP synthase (DXS) and farnesyl pyrophosphate synthase (FPPS). The approaches for engineering the active sites are subsequently explained. It will be helpful to construct the functionally improved enzymes for the high-yield production of terpenoid-based anticancer and antiviral metabolites, which would help to reduce the cost and improve the availability of such therapeutics for the humankind.

Recent advances of metabolic engineering strategies in natural isoprenoid production using cell factories

This review covers the strategies mostly developed in the last three years for microbial production of isoprenoid, classified according to the engineering targets.

Metabolic engineering of Escherichia coli for the production of isoprenoids

Metabolic engineering is the practice of using directed genetic manipulations to rewire cellular metabolism primarily with the aim to transform the organism into a single-celled chemical factory. Using biological processes, we can produce more complex chemicals in a more sustainable way. This is particularly important for chemicals which are hard to synthesize using traditional chemistry. However, cells have evolved for growth and must be engineered to produce a single chemical at commercially viable levels. This review focuses on the strategies used to rewire cellular metabolism to produce chemicals using isoprenoid production in Escherichia coli as an example that illustrates many of the challenges faced in metabolic engineering.

Reconstruction and Analysis of a Genome-Scale Metabolic Model of Ganoderma lucidum for Improved Extracellular Polysaccharide Production

In this study, we reconstructed for the first time a genome-scale metabolic model (GSMM) of Ganoderma lucidum strain CGMCC5.26, termed model iZBM1060, containing 1060 genes, 1202 metabolites, and 1404 reactions. Important findings based on model iZBM1060 and its predictions are as follows: (i) The extracellular polysaccharide (EPS) biosynthetic pathway was elucidated completely. (ii) A new fermentation strategy is proposed: addition of phenylalanine increased EPS production by 32.80% in simulations and by 38.00% in experiments. (iii) Eight genes for key enzymes were proposed for EPS overproduction. Model iZBM1060 provides a useful platform for regulating EPS production in terms of system metabolic engineering for G. lucidum, as well as a guide for future metabolic pathway construction of other high value-added edible/ medicinal mushroom species.

Metabolic network model guided engineering ethylmalonyl-CoA pathway to improve ascomycin production in Streptomyces hygroscopicus var. ascomyceticus

Background

Ascomycin is a 23-membered polyketide macrolide with high immunosuppressant and antifungal activity. As the lower production in bio-fermentation, global metabolic analysis is required to further explore its biosynthetic network and determine the key limiting steps for rationally engineering. To achieve this goal, an engineering approach guided by a metabolic network model was implemented to better understand ascomycin biosynthesis and improve its production.

Results

The metabolic conservation of Streptomyces species was first investigated by comparing the metabolic enzymes of Streptomyces coelicolor A3(2) with those of 31 Streptomyces strains, the results showed that more than 72% of the examined proteins had high sequence similarity with counterparts in every surveyed strain. And it was found that metabolic reactions are more highly conserved than the enzymes themselves because of its lower diversity of metabolic functions than that of genes. The main source of the observed metabolic differences was from the diversity of secondary metabolism. According to the high conservation of primary metabolic reactions in Streptomyces species, the metabolic network model of Streptomyces hygroscopicus var. ascomyceticus was constructed based on the latest reported metabolic model of S. coelicolor A3(2) and validated experimentally. By coupling with flux balance analysis and using minimization of metabolic adjustment algorithm, potential targets for ascomycin overproduction were predicted. Since several of the preferred targets were highly associated with ethylmalonyl-CoA biosynthesis, two target genes hcd (encoding 3-hydroxybutyryl-CoA dehydrogenase) and ccr (encoding crotonyl-CoA carboxylase/reductase) were selected for overexpression in S. hygroscopicus var. ascomyceticus FS35. Both the mutants HA-Hcd and HA-Ccr showed higher ascomycin titer, which was consistent with the model predictions. Furthermore, the combined effects of the two genes were evaluated and the strain HA-Hcd-Ccr with hcd and ccr overexpression exhibited the highest ascomycin production (up to 438.95 mg/L), 1.43-folds improvement than that of the parent strain FS35 (305.56 mg/L).

Conclusions

The successful constructing and experimental validation of the metabolic model of S. hygroscopicus var. ascomyceticus showed that the general metabolic network model of Streptomyces species could be used to analyze the intracellular metabolism and predict the potential key limiting steps for target metabolites overproduction. The corresponding overexpression strains of the two identified genes (hcd and ccr) using the constructed model all displayed higher ascomycin titer. The strategy for yield improvement developed here could also be extended to the improvement of other secondary metabolites in Streptomyces species.

Electronic supplementary material

The online version of this article (doi:10.1186/s12934-017-0787-5) contains supplementary material, which is available to authorized users.

Reconstruction of a Genome-scale Metabolic Network of Komagataeibacter nataicola RZS01 for Cellulose Production

Bacterial cellulose (BC) is widely used in industries owing to its high purity and strength. Although Komagataeibacter nataicola is a representative species for BC production, its intracellular metabolism leading to BC secretion is unclear. In the present study, a genome-scale metabolic network of cellulose-producing K. nataicola strain RZS01 was reconstructed to understand its metabolic behavior. This model iHZ771 comprised 771 genes, 2035 metabolites, and 2014 reactions. Constraint-based analysis was used to characterize and evaluate the critical intracellular pathways. The analysis revealed that a total of 71 and 30 genes are necessary for cellular growth in a minimal medium and complex medium, respectively. Glycerol was identified as the optimal carbon source for the highest BC production. The minimization of metabolic adjustment algorithm identified 8 genes as potential targets for over-production of BC. Overall, model iHZ771 proved to be a useful platform for understanding the physiology and BC production of K. nataicola.

Genomics of lactic acid bacteria: Current status and potential applications

Lactic acid bacteria (LAB) are widely used for the production of a variety of foods and feed raw materials where they contribute to flavor and texture of the fermented products. In addition, specific LAB strains are considered as probiotic due to their health-promoting effects in consumers. Recently, the genome sequencing of LAB is booming and the increased amount of published genomics data brings unprecedented opportunity for us to reveal the important traits of LAB. This review describes the recent progress on LAB genomics and special emphasis is placed on understanding the industry-related physiological features based on genomics analysis. Moreover, strategies to engineer metabolic capacity and stress tolerance of LAB with improved industrial performance are also discussed.

Enhancement of rapamycin production by metabolic engineering in Streptomyces hygroscopicus based on genome-scale metabolic model

Rapamycin, as a macrocyclic polyketide with immunosuppressive, antifungal, and anti-tumor activity produced by Streptomyces hygroscopicus, is receiving considerable attention for its significant contribution in medical field. However, the production capacity of the wild strain is very low. Hereby, a computational guided engineering approach was proposed to improve the capability of rapamycin production. First, a genome-scale metabolic model of Streptomyces hygroscopicus ATCC 29253 was constructed based on its annotated genome and biochemical information. The model consists of 1003 reactions, 711 metabolites after manual refinement. Subsequently, several potential genetic targets that likely guaranteed an improved yield of rapamycin were identified by flux balance analysis and minimization of metabolic adjustment algorithm. Furthermore, according to the results of model prediction, target gene pfk (encoding 6-phosphofructokinase) was knocked out, and target genes dahP (encoding 3-deoxy-d-arabino-heptulosonate-7-phosphate synthase) and rapK (encoding chorismatase) were overexpressed in the parent strain ATCC 29253. The yield of rapamycin increased by 30.8% by knocking out gene pfk and increased by 36.2 and 44.8% by overexpression of rapK and dahP, respectively, compared with parent strain. Finally, the combined effect of the genetic modifications was evaluated. The titer of rapamycin reached 250.8 mg/l by knockout of pfk and co-expression of genes dahP and rapK, corresponding to a 142.3% increase relative to that of the parent strain. The relationship between model prediction and experimental results demonstrates the validity and rationality of this approach for target identification and rapamycin production improvement.

Application of theoretical methods to increase succinate production in engineered strains

Computational methods have enabled the discovery of non-intuitive strategies to enhance the production of a variety of target molecules. In the case of succinate production, reviews covering the topic have not yet analyzed the impact and future potential that such methods may have. In this work, we review the application of computational methods to the production of succinic acid. We found that while a total of 26 theoretical studies were published between 2002 and 2016, only 10 studies reported the successful experimental implementation of any kind of theoretical knowledge. None of the experimental studies reported an exact application of the computational predictions. However, the combination of computational analysis with complementary strategies, such as directed evolution and comparative genome analysis, serves as a proof of concept and demonstrates that successful metabolic engineering can be guided by rational computational methods.

Comprehensive reconstruction and in silico analysis of Aspergillus niger genome-scale metabolic network model that accounts for 1210 ORFs

Aspergillus niger is one of the most important cell factories for industrial enzymes and organic acids production. A comprehensive genome-scale metabolic network model (GSMM) with high quality is crucial for efficient strain improvement and process optimization. The lack of accurate reaction equations and gene-protein-reaction associations (GPRs) in the current best model of A. niger named GSMM iMA871, however, limits its application scope. To overcome these limitations, we updated the A. niger GSMM by combining the latest genome annotation and literature mining technology. Compared with iMA871, the number of reactions in iHL1210 was increased from 1,380 to 1,764, and the number of unique ORFs from 871 to 1,210. With the aid of our transcriptomics analysis, the existence of 63% ORFs and 68% reactions in iHL1210 can be verified when glucose was used as the only carbon source. Physiological data from chemostat cultivations, ¹³ C-labeled and molecular experiments from the published literature were further used to check the performance of iHL1210. The average correlation coefficients between the predicted fluxes and estimated fluxes from ¹³ C-labeling data were sufficiently high (above 0.89) and the prediction of cell growth on most of the reported carbon and nitrogen sources was consistent. Using the updated genome-scale model, we evaluated gene essentiality on synthetic and yeast extract medium, as well as the effects of NADPH supply on glucoamylase production in A. niger. In summary, the new A. niger GSMM iHL1210 contains significant improvements with respect to the metabolic coverage and prediction performance, which paves the way for systematic metabolic engineering of A. niger. Biotechnol. Bioeng. 2017;114: 685-695. © 2016 Wiley Periodicals, Inc.

Reconstruction and analysis of the genome-scale metabolic model of schizochytrium limacinum SR21 for docosahexaenoic acid production

Background

Schizochytrium limacinum SR21 is a potential industrial strain for docosahexaenoic acid (DHA) production that contains more than 30–40 % DHA among its total fatty acids.

Methods

To resolve the DHA biosynthesis mechanism and improve DHA production at a systematic level, a genomescale metabolic model (GSMM), named iCY1170_DHA, which contains 1769 reactions, 1659 metabolites, and 1170 genes, was reconstructed.

Results

Based on genome annotation results and literature reports, a new DHA synthesis pathway based on a polyketide synthase (PKS) system was detected in S. limacinum. Similarly to conventional fatty acid synthesis, the biosynthesis of DHA via PKS requires abundant acetyl-CoA and NADPH. The in silico addition of malate and citrate led to increases of 24.5 % and 37.1 % in DHA production, respectively. Moreover, based on the results predicted by the model, six amino acids were shown to improve DHA production by experiment. Finally, 30 genes were identified as potential targets for DHA over-production using a Minimization of Metabolic Adjustment algorithm.

Conclusions

The reconstructed GSMM, iCY1170_DHA, could be used to elucidate the mechanism by which DHA is synthesized in S. limacinum and predict the requirements of abundant acetyl-CoA and NADPH for DHA production as well as the enhanced yields achieved via supplementation with six amino acids, malate, and citrate.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2042-y) contains supplementary material, which is available to authorized users.

Transcriptomics-based strain optimization tool for designing secondary metabolite overproducing strains of Streptomyces coelicolor

In silico model-driven analysis using genome-scale model of metabolism (GEM) has been recognized as a promising method for microbial strain improvement. However, most of the current GEM-based strain design algorithms based on flux balance analysis (FBA) heavily rely on the steady-state and optimality assumptions without considering any regulatory information. Thus, their practical usage is quite limited, especially in its application to secondary metabolites overproduction. In this study, we developed a transcriptomics-based strain optimization tool (tSOT) in order to overcome such limitations by integrating transcriptomic data into GEM. Initially, we evaluated existing algorithms for integrating transcriptomic data into GEM using Streptomyces coelicolor dataset, and identified iMAT algorithm as the only and the best algorithm for characterizing the secondary metabolism of S. coelicolor. Subsequently, we developed tSOT platform where iMAT is adopted to predict the reaction states, and successfully demonstrated its applicability to secondary metabolites overproduction by designing actinorhodin (ACT), a polyketide antibiotic, overproducing strain of S. coelicolor. Mutants overexpressing tSOT targets such as ribulose 5-phosphate 3-epimerase and NADP-dependent malic enzyme showed 2 and 1.8-fold increase in ACT production, thereby validating the tSOT prediction. It is expected that tSOT can be used for solving other metabolic engineering problems which could not be addressed by current strain design algorithms, especially for the secondary metabolite overproductions.

Identification of a critical determinant that enables efficient fatty acid synthesis in oleaginous fungi

Microorganisms are valuable resources for lipid production. What makes one microbe but not the other able to efficiently synthesize and accumulate lipids is poorly understood. In the present study, global gene expression prior to and after the onset of lipogenesis was determined by transcriptomics using the oleaginous fungus Mortierella alpina as a model system. A core of 23 lipogenesis associated genes was identified and their expression patterns shared a high similarity among oleaginous microbes Chlamydomonas reinhardtii, Mucor circinelloides and Rhizopus oryzae but was dissimilar to the non-oleaginous Aspergillus nidulans. Unexpectedly, Glucose-6-phosphate dehydrogenase (G6PD) and 6-phosphogluconate dehydrogenase (PGD) in the pentose phosphate pathway (PPP) were found to be the NADPH producers responding to lipogenesis in the oleaginous microbes. Their role in lipogenesis was confirmed by a knockdown experiment. Our results demonstrate, for the first time, that the PPP plays a significant role during fungal lipogenesis. Up-regulation of NADPH production by the PPP, especially G6PD, may be one of the critical determinants that enables efficiently fatty acid synthesis in oleaginous microbes.

Reconstruction and analysis of a genome-scale metabolic model of the oleaginous fungus Mortierella alpina

Background

Mortierella alpina is an oleaginous fungus used in the industrial scale production of arachidonic acid (ARA). In order to investigate the metabolic characteristics at a systems level and to explore potential strategies for enhanced lipid production, a genome-scale metabolic model of M. alpina was reconstructed.

Results

This model included 1106 genes, 1854 reactions and 1732 metabolites. On minimal growth medium, 86 genes were identified as essential, whereas 49 essential genes were identified on yeast extract medium. A series of sequential desaturase and elongase catalysed steps are involved in the synthesis of polyunsaturated fatty acids (PUFAs) from acetyl-CoA precursors, with concomitant NADPH consumption, and these steps were investigated in this study. Oxygen is known to affect the degree of unsaturation of PUFAs, and robustness analysis determined that an oxygen uptake rate of 2.0 mmol gDW⁻¹ h⁻¹ was optimal for ARA accumulation. The flux of 53 reactions involving NADPH was significantly altered at different ARA levels. Of these, malic enzyme (ME) was confirmed as a key component in ARA production and NADPH generation. When using minimization of metabolic adjustment, a knock-out of ME led to a 38.28% decrease in ARA production.

Conclusions

The simulation results confirmed the model as a useful tool for future research on the metabolism of PUFAs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-014-0137-8) contains supplementary material, which is available to authorized users.

Identification of novel knockout and up-regulated targets for improving isoprenoid production in E. coli

Discovery of novel potential genetic targets to increase the supply of isoprenoid precursors, isopentyl/dimethylallyl diphosphate, is of importance for microbial production of isoprenoids. Here, to improve isoprenoid precursor supply, a flux distribution comparison analysis, based on the genome-scale model, was utilized to simultaneously predict the knockout, down- and up-regulated targets in Escherichia coli. 51 targets were in silico discovered. All knockout and up-regulated targets were experimentally tested to enhance lycopene production. Five knockout targets (deoB, yhfw, yahI, pta and eutD) and four up-regulated targets (ompN, ompE, ndk and cmk) led to 10-45% increases of lycopene yield, respectively, which had not been uncovered in previous studies. When engineering of the five most significant targets gdhA, eutD, tpiA, ompE and ompN, were combined the lycopene titer improved by 174% in shake-flask and 81% in bioreactor fermentations with a maximum yield of 454 mg l(-1).

Biosynthesis of Taxadiene in Saccharomyces cerevisiae : selection of geranylgeranyl diphosphate synthase directed by a computer-aided docking strategy

Identification of efficient key enzymes in biosynthesis pathway and optimization of the fitness between functional modules and chassis are important for improving the production of target compounds. In this study, the taxadiene biosynthesis pathway was firstly constructed in yeast by transforming ts gene and overexpressing erg20 and thmgr. Then, the catalytic capabilities of six different geranylgeranyl diphosphate synthases (GGPPS), the key enzyme in mevalonic acid (MVA) pathway catalyzing famesyl diphosphate (FPP) to geranylgeranyl diphosphate (GGPP), were predicted using enzyme-substrate docking strategy. GGPPSs from Taxus baccata x Taxus cuspidate (GGPPSbc), Erwinia herbicola (GGPPSeh), and S. cerevisiae (GGPPSsc) which ranked 1st, 4th and 6th in docking with FPP were selected for construction. The experimental results were consistent with the computer prediction that the engineered yeast with GGPPSbc exhibited the highest production. In addition, two chassis YSG50 and W303-1A were chosen, and the titer of taxadiene reached 72.8 mg/L in chassis YSG50 with GGPPSbc. Metabolomic study revealed that the contents of tricarboxylic acid cycle (TCA) intermediates and their precursor amino acids in chassis YSG50 was lower than those in W303-1A, indicating less carbon flux was divided into TCA cycle. Furthermore, the levels of TCA intermediates in the taxadiene producing yeasts were lower than those in chassis YSG50. Thus, it may result in more carbon flux in MVA pathway in chassis YSG50, which suggested that YSG50 was more suitable for engineering the taxadiene producing yeast. These results indicated that computer-aided protein modeling directed isoenzyme selection strategy and metabolomic study could guide the rational design of terpenes biosynthetic cells.

Current and emerging options for taxol production

Paclitaxel (trademark "Taxol") is a plant-derived isoprenoid natural product that exhibits potent anticancer activity. Taxol was originally isolated from the Pacific yew tree in 1967 and triggered an intense scientific and engineering venture to provide the compound reliably to cancer patients. The choices available for production include synthetic and biosynthetic routes (and combinations thereof). This chapter focuses on the currently utilized and emerging biosynthetic options for Taxol production. A particular emphasis is placed on the biosynthetic production hosts including macroscopic and unicellular plant species and more recent attempts to elucidate, transfer, and reconstitute the Taxol pathway within technically advanced microbial hosts. In so doing, we provide the reader with relevant background related to Taxol and more general information related to producing valuable, but structurally complex, natural products through biosynthetic strategies.

Partial inhibition and bilevel optimization in flux balance analysis

MOTIVATION

Within Flux Balance Analysis, the investigation of complex subtasks, such as finding the optimal perturbation of the network or finding an optimal combination of drugs, often requires to set up a bilevel optimization problem. In order to keep the linearity and convexity of these nested optimization problems, an ON/OFF description of the effect of the perturbation (i.e. Boolean variable) is normally used. This restriction may not be realistic when one wants, for instance, to describe the partial inhibition of a reaction induced by a drug.

RESULTS

In this paper we present a formulation of the bilevel optimization which overcomes the oversimplified ON/OFF modeling while preserving the linear nature of the problem. A case study is considered: the search of the best multi-drug treatment which modulates an objective reaction and has the minimal perturbation on the whole network. The drug inhibition is described and modulated through a convex combination of a fixed number of Boolean variables. The results obtained from the application of the algorithm to the core metabolism of E.coli highlight the possibility of finding a broader spectrum of drug combinations compared to a simple ON/OFF modeling.

CONCLUSIONS

The method we have presented is capable of treating partial inhibition inside a bilevel optimization, without loosing the linearity property, and with reasonable computational performances also on large metabolic networks. The more fine-graded representation of the perturbation allows to enlarge the repertoire of synergistic combination of drugs for tasks such as selective perturbation of cellular metabolism. This may encourage the use of the approach also for other cases in which a more realistic modeling is required.

From flavors and pharmaceuticals to advanced biofuels: production of isoprenoids in Saccharomyces cerevisiae

Isoprenoids denote the largest group of chemicals in the plant kingdom and are employed for a wide range of applications in the food and pharmaceutical industry. In recent years, isoprenoids have additionally been recognized as suitable replacements for petroleum-derived fuels and could thus promote the transition towards a more sustainable society. To realize the biofuel potential of isoprenoids, a very efficient production system is required. While complex chemical structures as well as the low abundance in nature demonstrate the shortcomings of chemical synthesis and plant extraction, isoprenoids can be produced by genetically engineered microorganisms from renewable carbon sources. In this article, we summarize the development of isoprenoid applications from flavors and pharmaceuticals to advanced biofuels and review the strategies to design microbial cell factories, focusing on Saccharomyces cerevisiae for the production of these compounds. While the high complexity of biosynthetic pathways and the toxicity of certain isoprenoids still denote challenges that need to be addressed, metabolic engineering has enabled large-scale production of several terpenoids and thus, the utilization of these compounds is likely to expand in the future.

Biochemical strategies for enhancing the in vivo production of natural products with pharmaceutical potential

Natural products have been associated with significant health benefits in preventing and treating various chronic human diseases such as cancer, cardiovascular diseases, diabetes, Alzheimer's disease, and pathogenic infections. However, the isolation, characterization and evaluation of natural products remain a challenge, mainly due to their limited bioavailability. Metabolic engineering and fermentation technology have emerged as alternative approaches for generating natural products under controlled conditions that can be optimized to maximize yields. Optimization of these processes includes the evaluation of factors such as host selection, product biosynthesis interaction with the cell's central metabolism, product degradation, and byproduct formation. This review summarizes the most recent biochemical strategies and advances in expanding and diversifying natural compounds as well as maximizing their production in microbial and plants cells.

Metabolic engineering and in vitro biosynthesis of phytochemicals and non-natural analogues

Over the years, natural products from plants and their non-natural derivatives have shown to be active against different types of chronic diseases. However, isolation of such natural products can be limited due to their low bioavailability, and environmental restrictions. To address these issues, in vivo and in vitro reconstruction of plant metabolic pathways and the metabolic engineering of microbes and plants have been used to generate libraries of compounds. Significant advances have been made through metabolic engineering of microbes and plant cells to generate a variety of compounds (e.g. isoprenoids, flavonoids, or stilbenes) using a diverse array of methods to optimize these processes (e.g. host selection, operational variables, precursor selection, gene modifications). These approaches have been used also to generate non-natural analogues with different bioactivities. In vitro biosynthesis allows the synthesis of intermediates as well as final products avoiding post-translational limitations. Moreover, this strategy allows the use of substrates and the production of metabolites that could be toxic for cells, or expand the biosynthesis into non-conventional media (e.g. organic solvents, supercritical fluids). A perspective is also provided on the challenges for generating novel chemical structures and the potential of combining metabolic engineering and in vitro biocatalysis to produce metabolites with more potent biological activities.

Genome-scale metabolic network guided engineering of Streptomyces tsukubaensis for FK506 production improvement

Background

FK506 is an important immunosuppressant, which can be produced by Streptomyces tsukubaensis. However, the production capacity of the strain is very low. Hereby, a computational guided engineering approach was proposed in order to improve the intracellular precursor and cofactor availability of FK506 in S. tsukubaensis.

Results

First, a genome-scale metabolic model of S. tsukubaensis was constructed based on its annotated genome and biochemical information. Subsequently, several potential genetic targets (knockout or overexpression) that guaranteed an improved yield of FK506 were identified by the recently developed methodology. To validate the model predictions, each target gene was manipulated in the parent strain D852, respectively. All the engineered strains showed a higher FK506 production, compared with D852. Furthermore, the combined effect of the genetic modifications was evaluated. Results showed that the strain HT-ΔGDH-DAZ with gdhA-deletion and dahp-, accA2-, zwf2-overexpression enhanced FK506 concentration up to 398.9 mg/L, compared with 143.5 mg/L of the parent strain D852. Finally, fed-batch fermentations of HT-ΔGDH-DAZ were carried out, which led to the FK506 production of 435.9 mg/L, 1.47-fold higher than the parent strain D852 (158.7 mg/L).

Conclusions

Results confirmed that the promising targets led to an increase in FK506 titer. The present work is the first attempt to engineer the primary precursor pathways to improve FK506 production in S. tsukubaensis with genome-scale metabolic network guided metabolic engineering. The relationship between model prediction and experimental results demonstrates the rationality and validity of this approach for target identification. This strategy can also be applied to the improvement of other important secondary metabolites.

The early stages of taxol biosynthesis: an interim report on the synthesis and identification of early pathway metabolites

The biosynthesis of the anti-cancer drug taxol (paclitaxel) has required the collaborative efforts of several research groups to tackle the synthesis and labeling of putative biosynthetic intermediates, in concert with the identification, cloning and functional expression of the biosynthetic genes responsible for the construction of this complex natural product. Based on a combination of precursor labeling and incorporation experiments, and metabolite isolation from Taxus spp., a picture of the complex matrix of pathway oxygenation reactions following formation of the first committed intermediate, taxa-4(5),11(12)-diene, is beginning to emerge. An overview of the current state of knowledge on the early-stages of taxol biosynthesis is presented.

Toward biosynthetic design and implementation of Escherichia coli-derived paclitaxel and other heterologous polyisoprene compounds

Escherichia coli offers unparalleled engineering capacity in the context of heterologous natural product biosynthesis. However, as with other heterologous hosts, cellular metabolism must be designed or redesigned to support final compound formation. This task is at once complicated and aided by the fact that the cell does not natively produce an abundance of natural products. As a result, the metabolic engineer avoids complicated interactions with native pathways closely associated with the outcome of interest, but this convenience is tempered by the need to implement the required metabolism to allow functional biosynthesis. This review focuses on engineering E. coli for the purpose of polyisoprene formation, as it is related to isoprenoid compounds currently being pursued through a heterologous approach. In particular, the review features the compound paclitaxel and early efforts to design and overproduce intermediates through E. coli.