Model-driven evaluation of the production potential for growth-coupled products of Escherichia coli

Integrated approaches utilizing in silico analyses will be necessary to successfully advance the field of metabolic engineering. Here, we present an integrated approach through a systematic model-driven evaluation of the production potential for the bacterial production organism Escherichia coli to produce multiple native products from different representative feedstocks through coupling metabolite production to growth rate. Designs were examined for 11 unique central metabolism and amino acid targets from three different substrates under aerobic and anaerobic conditions. Optimal strain designs were reported for designs which possess maximum yield, substrate-specific productivity, and strength of growth-coupling for up to 10 reaction eliminations (knockouts). In total, growth-coupled designs could be identified for 36 out of the total 54 conditions tested, corresponding to eight out of the 11 targets. There were 17 different substrate/target pairs for which over 80% of the theoretical maximum potential could be achieved. The developed method introduces a new concept of objective function tilting for strain design. This study provides specific metabolic interventions (strain designs) for production strains that can be experimentally implemented, characterizes the potential for E. coli to produce native compounds, and outlines a strain design pipeline that can be utilized to design production strains for additional organisms.     

Generation of an atlas for commodity chemical production in Escherichia coli and a novel pathway prediction algorithm,GEM-Path

The production of 75% of the current drug molecules and 35% of all chemicals could be achieved through bioprocessing (Arundel and Sawaya, 2009). To accelerate the transition from a petroleum-based chemical industry to a sustainable bio-based industry, systems metabolic engineering has emerged to computationally design metabolic pathways for chemical production. Although algorithms able to provide specific metabolic interventions and heterologous production pathways are available, a systematic analysis for all possible production routes to commodity chemicals in Escherichia coli is lacking. Furthermore, a pathway prediction algorithm that combines direct integration of genome-scale models at each step of the search to reduce the search space does not exist. Previous work (Feist et al., 2010) performed a model-driven evaluation of the growth-coupled production potential for E. coli to produce multiple native compounds from different feedstocks. In this study, we extended this analysis for non-native compounds by using an integrated approach through heterologous pathway integration and growth-coupled metabolite production design. In addition to integration with genome-scale model integration, the GEM-Path algorithm developed in this work also contains a novel approach to address reaction promiscuity. In total, 245 unique synthetic pathways for 20 large volume compounds were predicted. Host metabolism with these synthetic pathways was then analyzed for feasible growth-coupled production and designs could be identified for 1271 of the 6615 conditions evaluated. This study characterizes the potential for E. coli to produce commodity chemicals, and outlines a generic strain design workflow to design production strains.     

Enumeration of Smallest Intervention Strategies in Genome-Scale Metabolic Networks

One ultimate goal of metabolic network modeling is the rational redesign of biochemical networks to optimize the production of certain compounds by cellular systems. Although several constraint-based optimization techniques have been developed for this purpose, methods for systematic enumeration of intervention strategies in genome-scale metabolic networks are still lacking. In principle, Minimal Cut Sets (MCSs; inclusion-minimal combinations of reaction or gene deletions that lead to the fulfilment of a given intervention goal) provide an exhaustive enumeration approach. However, their disadvantage is the combinatorial explosion in larger networks and the requirement to compute first the elementary modes (EMs) which itself is impractical in genome-scale networks.We present MCSEnumerator, a new method for effective enumeration of the smallest MCSs (with fewest interventions) in genome-scale metabolic network models. For this we combine two approaches, namely (i) the mapping of MCSs to EMs in a dual network, and (ii) a modified algorithm by which shortest EMs can be effectively determined in large networks. In this way, we can identify the smallest MCSs by calculating the shortest EMs in the dual network. Realistic application examples demonstrate that our algorithm is able to list thousands of the most efficient intervention strategies in genome-scale networks for various intervention problems. For instance, for the first time we could enumerate all synthetic lethals in E.coli with combinations of up to 5 reactions. We also applied the new algorithm exemplarily to compute strain designs for growth-coupled synthesis of different products (ethanol, fumarate, serine) by E.coli. We found numerous new engineering strategies partially requiring less knockouts and guaranteeing higher product yields (even without the assumption of optimal growth) than reported previously. The strength of the presented approach is that smallest intervention strategies can be quickly calculated and screened with neither network size nor the number of required interventions posing major challenges.     

Computational design and analysis of modular cells for large libraries of exchangeable product synthesis modules

Microbial metabolism can be harnessed to produce a large library of useful chemicals from renewable resources such as plant biomass. However, it is laborious and expensive to create microbial biocatalysts to produce each new product. To tackle this challenge, we have recently developed modular cell (ModCell) design principles that enable rapid generation of production strains by assembling a modular (chassis) cell with exchangeable production modules to achieve overproduction of target molecules. Previous computational ModCell design methods are limited to analyze small libraries of around 20 products. In this study, we developed a new computational method, named ModCell-HPC, that can design modular cells for large libraries with hundreds of products with a highly-parallel and multi-objective evolutionary algorithm and enable us to elucidate modular design properties. We demonstrated ModCell-HPC to design Escherichia coli modular cells towards a library of 161 endogenous production modules. From these simulations, we identified E. coli modular cells with few genetic manipulations that can produce dozens of molecules in a growth-coupled manner with different types of fermentable sugars. These designs revealed key genetic manipulations at the chassis and module levels to accomplish versatile modular cells, involving not only in the removal of major by-products but also modification of branch points in the central metabolism. We further found that the effect of various sugar degradation on redox metabolism results in lower compatibility between a modular cell and production modules for growth on pentoses than hexoses. To better characterize the degree of compatibility, we developed a method to calculate the minimal set cover, identifying that only three modular cells are all needed to couple with all compatible production modules. By determining the unknown compatibility contribution metric, we further elucidated the design features that allow an existing modular cell to be re-purposed towards production of new molecules. Overall, ModCell-HPC is a useful tool for understanding modularity of biological systems and guiding more efficient and generalizable design of modular cells that help reduce research and development cost in biocatalysis.     

In silico design and adaptive evolution of Escherichia coli for production of lactic acid

The development and validation of new methods to help direct rational strain design for metabolite overproduction remains an important problem in metabolic engineering. Here we show that computationally predicted E. coli strain designs, calculated from a genome-scale metabolic model, can lead to successful production strains and that adaptive evolution of the engineered strains can lead to improved production capabilities. Three strain designs for lactate production were implemented yielding a total of 11 evolved production strains that were used to demonstrate the utility of this integrated approach. Strains grown on 2 g/L glucose at 37 degrees C showed lactate titers ranging from 0.87 to 1.75 g/L and secretion rates that were directly coupled to growth rates.     

Kinetics of microbial product formation and its consequences for the optimization of fermentation processes

Different types of product formation kinetics are discussed with respect to their significance for fermentation process economics. Microbial products belonging to various classes are formed in a growth-coupled manner. It is often found that the specific rate of product formation increases with the specific growth rate, approaching a maximum at higher growth rates. It is illustrated that for such types of relationship between the product formation rate and the growth rate process conditions are optimal when the specific rate of product formation is about half-maximal.     

Synthesis and biological evaluation of hydroxamate-Based inhibitors of glutamate carboxypeptidase II

A series of hydroxamic acids has been prepared as potential inhibitors of glutamate carboxypeptidase II (GCP II). Compounds based on a P1' residue (primed-side inhibitors) were more potent than those based on a P1 group (unprimed-side inhibitors). Inhibitory potency of the primed-side GCP II inhibitors was found to be dependent on the number of methylene units between the hydroxamate group and pentanedioic acid. Succinyl hydroxamic acid derivative, 2-(hydroxycarbamoylmethyl)pentanedioic acid, is the most potent GCP II inhibitor with an IC(50) value of 220nM. The comparison of the results to those of other classes of GCP II inhibitors as well as hydroxamate-based MMP inhibitors provides further insight into the structure-activity relationships of GCP II inhibition.     

Reflections on clinical research in sub-Saharan Africa

The urgent need for new, safe and sustainable interventions against diseases that disproportionally affect the poor is finally receiving global attention and the funding landscape for development projects has significantly improved during the past decade. For the development of new drug and vaccine candidates, clinical trials have become the most important tool to assess their safety and efficacy. Recently, there has been a seismic shift in the number of clinical trials conducted in resource-limited settings. We discuss the current framework of clinical research in sub-Saharan Africa, from building product pipelines to the capacities needed for the conduct of trials according the harmonised Good Clinical Practice (GCP) ICH E6 guideline. We place emphasis on clinical research in neglected tropical diseases which still frequently has to be conducted with limited financial, logistical and human resources. Given those short-comings we recommend minimum standards needed at the local, national and sponsor levels to provide GCP-compliant clinical research.     

In silico strategies to couple production of bioethanol with growth in cyanobacteria

Cyanobacteria have been considered as promising candidates for sustainable bioproduction from inexpensive raw materials, as they grow on light, carbon dioxide, and minimal inorganic nutrients. In this study, we present a genome-scale metabolic network model for Synechocystis sp. PCC 6803 and study the optimal design of the strain for ethanol production by using a mixed integer linear problem reformulation of a bilevel programming problem that identifies gene knockouts which lead to coupling between growth and product synthesis. Five mutants were found, where the in silico model predicts coupling between biomass growth and ethanol production in photoautotrophic conditions. The best mutant gives an in silico ethanol production of 1.054 mmol·gDW ⁻¹·h ⁻¹.     

Optimum bacteriocin production by Lactobacillus plantarum 17.2b requires absence of NaCl and apparently follows a mixed metabolite kinetics

Bacteriocins from lactic acid bacteria are ribosomally synthesized anti-microbial compounds that may find applications from food preservation to healthcare. Food preservation, using in situ bacteriocin production is the most obvious and simple. Frequently, the best conditions for bacteriocin production are those prevailing during food fermentation but a better understanding of the relationship between growth and bacteriocin production is required. In this work, we evaluate the effects of some environmental factors on bacteriocin production by Lactobacillus plantarum 17.2b. A first screening design showed that NaCl, temperature, pH and the type and concentration of carbon and nitrogen sources were most influents. A moderate stimulatory effect of ethanol and oleuropein was also registered. Two consecutive central composite designs were used to examine the effect of the selected variables and to compute its optimum. The evolution of changes produced by the alterations in environmental factors was further examined trough perturbation plots. Bacteriocin production by L. plantarum 17.2b was very sensitive to environmental conditions and uncoupled from growth. Maximum production required suboptimal growth temperatures, pH values above growth's optimum and no NaCl. A preliminary approach to kinetics showed that bacteriocin production by this strain apparently follows mixed metabolite kinetics.     

Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using large-scale kinetic models

Rational metabolic engineering methods are increasingly employed in designing the commercially viable processes for the production of chemicals relevant to pharmaceutical, biotechnology, and food and beverage industries. With the growing availability of omics data and of methodologies capable to integrate the available data into models, mathematical modeling and computational analysis are becoming important in designing recombinant cellular organisms and optimizing cell performance with respect to desired criteria. In this contribution, we used the computational framework ORACLE (Optimization and Risk Analysis of Complex Living Entities) to analyze the physiology of recombinant Escherichia coli producing 1,4-butanediol (BDO) and to identify potential strategies for improved production of BDO. The framework allowed us to integrate data across multiple levels and to construct a population of large-scale kinetic models despite the lack of available information about kinetic properties of every enzyme in the metabolic pathways. We analyzed these models and we found that the enzymes that primarily control the fluxes leading to BDO production are part of central glycolysis, the lower branch of tricarboxylic acid (TCA) cycle and the novel BDO production route. Interestingly, among the enzymes between the glucose uptake and the BDO pathway, the enzymes belonging to the lower branch of TCA cycle have been identified as the most important for improving BDO production and yield. We also quantified the effects of changes of the target enzymes on other intracellular states like energy charge, cofactor levels, redox state, cellular growth, and byproduct formation. Independent earlier experiments on this strain confirmed that the computationally obtained conclusions are consistent with the experimentally tested designs, and the findings of the present studies can provide guidance for future work on strain improvement. Overall, these studies demonstrate the potential and effectiveness of ORACLE for the accelerated design of microbial cell factories.     

Activity-Based Environmental Inventory Allocation

This article presents a generic method to assist product and process designers in measuring resource use and environmental discharges based on the relationships between process flow inputs and outputs and their activity levels. It combines activity-based costing from conventional accounting with life-cycle inventories. The method is demonstrated on four electronic assembly product and process designs. The demonstration exhibits the disaggregation and allocation of costs and effluents from various manufacturing operations. This activity-based environmental allocation approach may be integrated with inventory analysis-the first step in full and streamlined life-cycle assessments, design for environment evaluation methods, environmental management activities, and new production planning models that consider environmental impacts.     

The role of arginine 47 in the cyclization and coupling reactions of cyclodextrin glycosyltransferase from Bacillus circulans strain 251 implications for product inhibition and product specificity.

Cyclodextrin glycosyltransferase (CGTase) (EC 2.4.1.19) is used for the industrial production of cyclodextrins. Its application, however, is hampered by the limited cyclodextrin product specificity and the strong inhibitory effect of cyclodextrins on CGTase activity. Recent structural studies have identified Arg47 in the Bacillus circulans strain 251 CGTase as an active-site residue interacting with cyclodextrins, but not with linear oligosaccharides. Arg47 thus may specifically affect CGTase reactions with cyclic substrates or products. Here we show that mutations in Arg47 (to Leu or Gln) indeed have a negative effect on the cyclization and coupling activities; Arg47 specifically stabilizes the oligosaccharide chain in the transition state for these reactions. As a result, the mutant proteins display a shift in product specificity towards formation of larger cyclodextrins. As expected, both mutants also showed lower affinities for cyclodextrins in the coupling reaction, and a reduced competitive (product) inhibition of the disproportionation reaction by cyclodextrins. Both mutants also provide valuable information about the processes taking place during cyclodextrin production assays. Mutant Arg47-->Leu displayed an increased hydrolyzing activity, causing accumulation of increasing amounts of short oligosaccharides in the reaction mixture, which resulted in lower final amounts of cyclodextrins produced from starch. Interestingly, mutant Arg47-->Gln displayed an increased ratio of cyclization/coupling and a decreased hydrolyzing activity. Due to the decreased coupling activity, which especially affects the production of larger cyclodextrins, this CGTase variant produced the various cyclodextrins in a stable ratio in time. This feature is very promising for the industrial application of CGTase enzymes with improved product specificity.     

Optimization of astaxanthin production by Phaffia rhodozyma through factorial design and response surface methodology.

Sequential methodology based on the application of three types of experimental designs was used to optimize the astaxanthin production of the mutant strain 25-2 of Phaffia rhodozyma in shake flask cultures. The first design employed was a factorial design 2(5), where the factors studied were: pH, temperature, percent of inoculum, carbon and nitrogen concentrations, each one at two levels. This design was performed in two medium types: rich YM medium and minimal medium, based on date juice (Yucca medium). With this first design the most important factors were determined (carbon concentration and temperature) that were used in the second experimental strategy: the method of steepest ascent was applied in order to rapidly approach the optimum. Finally, a second-order response surface design was applied using temperature and carbon concentration as factors. The optimal conditions stimulating the highest astaxanthin production were: 19.7 degrees C temperature; 11.25 g l(-1) carbon concentration; 6.0 pH; 5% inoculum and 0.5 g l(-1) nitrogen concentration. Under these conditions the astaxanthin production was 8100 microg l(-1), 92% higher than the production under the initial conditions.     

Isoflavone aglycon produced by culture of soybean extracts with basidiomycetes and its anti-angiogenic activity

Soybean extracts (SBE) containing isoflavone glycosides were cultured with Ganoderma lucidum mycelia producing beta-glucosidase. The anti-angiogenic effects of the cultivated product, containing rich in genistein, named GCP (genistein combined polysaccharide), were assessed with chick chorioallantoic membranes (CAM) and a mouse dorsal air-sac model. Beta-glucosidase produced by the mycelia converted the isoflavone glycosides into aglycons. A test of volunteers showed that serum concentrations of genistein in the subjects treated with GCP (n = 4) at 3 h after administration were significantly higher than those in the subjects treated with SBE (n = 4). GCP inhibited angiogenesis in CAM, and the activity of GCP was greater than that of SBE. GCP inhibited the formation of new vessels induced by colon carcinoma cells in vivo.     

6-deoxyerythronolide B production through chromosomal localization of the deoxyerythronolide B synthase genes in E. coli

Chromosomal engineering was used to localize the deoxyerythronolide B synthase (DEBS) genes and propionyl-CoA carboxylase (PCC) genes to the BAP1 Escherichia coli chromosome creating the new strain YW9. YW9 then featured a plasmid-free heterologous pathway for the production of the polyketide product 6-deoxyerythronolide B (6dEB, a precursor to the antibiotic erythromycin) highlighted by the successful chromosomal integration of five genes total and three DEBS genes each approximately 10 kb in length. The new strain was tested for small-scale 6dEB biosynthesis and compared to 6dEB production from plasmid-derived gene expression at 22, 30, and 37 degrees C. YW9 produced 6dEB at each temperature tested; whereas, the current plasmid-based system could only produce 6dEB at 22 and 30 degrees C. As determined by MS analysis, average production levels for YW9 were 0.47 (22 degrees C), 0.52 (30 degrees C), and 0.11 (37 degrees C)mg/L.     

EMILiO: a fast algorithm for genome-scale strain design

Systems-level design of cell metabolism is becoming increasingly important for renewable production of fuels, chemicals, and drugs. Computational models are improving in the accuracy and scope of predictions, but are also growing in complexity. Consequently, efficient and scalable algorithms are increasingly important for strain design. Previous algorithms helped to consolidate the utility of computational modeling in this field. To meet intensifying demands for high-performance strains, both the number and variety of genetic manipulations involved in strain construction are increasing. Existing algorithms have experienced combinatorial increases in computational complexity when applied toward the design of such complex strains. Here, we present EMILiO, a new algorithm that increases the scope of strain design to include reactions with individually optimized fluxes. Unlike existing approaches that would experience an explosion in complexity to solve this problem, we efficiently generated numerous alternate strain designs producing succinate, l-glutamate and l-serine. This was enabled by successive linear programming, a technique new to the area of computational strain design.     

Isoflavone Aglycon Produced by Culture of Soybean Extracts with Basidiomycetes and Its Anti-angiogenic Activity

Soybean extracts (SBE) containing isoflavone glycosides were cultured with Ganoderma lucidum mycelia producing β-glucosidase. The anti-angiogenic effects of the cultivated product, containing rich in genistein, named GCP (genistein combined polysaccharide), were assessed with chick chorioallantoic membranes (CAM) and a mouse dorsal air-sac model. β-Glucosidase produced by the mycelia converted the isoflavone glycosides into aglycons. A test of volunteers showed that serum concentrations of genistein in the subjects treated with GCP (n=4) at 3 h after administration were significantly higher than those in the subjects treated with SBE (n=4).

GCP inhibited angiogenesis in CAM, and the activity of GCP was greater than that of SBE. GCP inhibited the formation of new vessels induced by colon carcinoma cells in vivo.