A metabolic network stoichiometry analysis of microbial growth and product formation

Metabolism: How a eukaryote adapted to life without respiration

A new study finds that Schizosaccharomyces japonicus, a eukaryote that lost the ability to respire, modified its central carbon metabolism to maintain efficient ATP production, cofactor regeneration, and amino-acid production. This remarkable metabolic flexibility opens new avenues towards applications.

Optimization of energy production and central carbon metabolism in a non-respiring eukaryote

Most eukaryotes respire oxygen, using it to generate biomass and energy. However, a few organisms have lost the capacity to respire. Understanding how they manage biomass and energy production may illuminate the critical points at which respiration feeds into central carbon metabolism and explain possible routes to its optimization. Here, we use two related fission yeasts, Schizosaccharomyces pombe and Schizosaccharomyces japonicus, as a comparative model system. We show that although S. japonicus does not respire oxygen, unlike S. pombe, it is capable of efficient NADH oxidation, amino acid synthesis, and ATP generation. We probe possible optimization strategies through the use of stable isotope tracing metabolomics, mass isotopologue distribution analysis, genetics, and physiological experiments. S. japonicus appears to have optimized cytosolic NADH oxidation via glycerol-3-phosphate synthesis. It runs a fully bifurcated TCA pathway, sustaining amino acid production. Finally, we propose that it has optimized glycolysis to maintain high ATP/ADP ratio, in part by using the pentose phosphate pathway as a glycolytic shunt, reducing allosteric inhibition of glycolysis and supporting biomass generation. By comparing two related organisms with vastly different metabolic strategies, our work highlights the versatility and plasticity of central carbon metabolism in eukaryotes, illuminating critical adaptations supporting the preferential use of glycolysis over oxidative phosphorylation.

Alleviating glucose repression and enhancing respiratory capacity to increase itaconic acid production

The Crabtree effect products ethanol and acetic acid can be used for itaconic acid (IA) production in Saccharomyces cerevisiae. However, both the IA synthesis and oxidative phosphorylation pathways were hampered by glucose repression when glucose was used as the substrate. This study aimed to improve IA titer by increasing gene expressions related to glucose derepression without impairing yeast growth on glucose. Engineering the acetyl-CoA synthesis pathway increased the titer of IA to 257 mg/L in a urea-based medium. Instead of entire pathway overexpression, we found that some signaling pathways regulating glucose repression were effective targets to improve IA production and respiratory capacity. As a consequence of the reduced inhibition, IA titer was further increased by knocking out a negative regulator of the mitochondrial retrograde signaling MKS1. SNF1/MIG1 signaling was disturbed by deleting the hexokinase HXK2 or an endoplasmic reticulum membrane protein GSF2. The shaking results showed that XYY286 (BY4741, HO::cadA, Y::Dz.ada, 208a::Mt.acs, Δhxk2, pRS415-cadA, pRS423-aac2) accumulated 535 mg/L IA in 168 h in the YSCGLU medium. qRT-PCR results verified that deletion of MKS1 or HXK2 upregulated the gene expressions of the IA synthesis and respiratory pathways during the growth on glucose.

Rewiring regulation on respiro-fermentative metabolism relieved Crabtree effects in Saccharomyces cerevisiae

The respiro-fermentative metabolism in the yeast Saccharomyces cerevisiae, also called the Crabtree effect, results in lower energy efficiency and biomass yield which can impact yields of chemicals to be produced using this cell factory. Although it can be engineered to become Crabtree negative, the slow growth and glucose consumption rate limit its industrial application. Here the Crabtree effect in yeast can be alleviated by engineering the transcription factor Mth1 involved in glucose signaling and a subunit of the RNA polymerase II mediator complex Med2. It was found that the mutant with the MTH1^A81D&MED2*^432Y allele could grow in glucose rich medium with a specific growth rate of 0.30 h⁻¹, an ethanol yield of 0.10 g g⁻¹, and a biomass yield of 0.21 g g⁻¹, compared with a specific growth rate of 0.40 h⁻¹, an ethanol yield of 0.46 g g⁻¹, and a biomass yield of 0.11 g g⁻¹ in the wild-type strain CEN.PK 113-5D. Transcriptome analysis revealed significant downregulation of the glycolytic process, as well as the upregulation of the TCA cycle and the electron transfer chain. Significant expression changes of several reporter transcription factors were also identified, which might explain the higher energy efficiencies in the engineered strain. We further demonstrated the potential of the engineered strain with the production of 3-hydroxypropionic acid at a titer of 2.04 g L⁻¹, i.e., 5.4-fold higher than that of a reference strain, indicating that the alleviated glucose repression could enhance the supply of mitochondrial acetyl-CoA. These results suggested that the engineered strain could be used as an efficient cell factory for mitochondrial production of acetyl-CoA derived chemicals.

Metabolic flux and transcriptome analyses provide insights into the mechanism underlying zinc sulfate improved β-1,3-D-glucan production by Aureobasidium pullulans

The effects of zinc sulfate at various concentrations on β-1,3-D-glucan (β-glucan) and pullulan production were investigated in flasks, and 0.1 g/L zinc sulfate was found to be the optimum concentration favoring increased β-glucan production. When batch culture of Aureobasidium pullulans CCTCC M 2012259 with 0.1 g/L zinc sulfate was carried out, the maximum dry biomass decreased by 16.9% while β-glucan production significantly increased by 120.5%, compared to results obtained from the control without zinc sulfate addition. To reveal the mechanism underlying zinc sulfate improved β-glucan production, both metabolic flux analysis and RNA-seq analysis were performed. The results indicated that zinc sulfate decreased carbon flux towards biomass formation and ATP supply, down-regulated genes associated with membrane part and cellular components organization, leading to a decrease in dry cell weight. However, zinc sulfate increased metabolic flux towards β-glucan biosynthesis, up-regulated genes related to glycan biosynthesis and nucleotide metabolism, resulting in improved β-glucan production. This study provides insights into the changes in the metabolism of A. pullulans in response to zinc sulfate, and can serve as a valuable reference of genetic information for improving the production of polysaccharides through metabolic engineering.

Towards model-driven characterization and manipulation of plant lipid metabolism

Plant lipids have versatile applications and provide essential fatty acids in human diet. Therefore, there has been a growing interest to better characterize the genetic basis, regulatory networks, and metabolic pathways that shape lipid quantity and composition. Addressing these issues is challenging due to context-specificity of lipid metabolism integrating environmental, developmental, and tissue-specific cues. Here we systematically review the known metabolic pathways and regulatory interactions that modulate the levels of storage lipids in oilseeds. We argue that the current understanding of lipid metabolism provides the basis for its study in the context of genome-wide plant metabolic networks with the help of approaches from constraint-based modeling and metabolic flux analysis. The focus is on providing a comprehensive summary of the state-of-the-art of modeling plant lipid metabolic pathways, which we then contrast with the existing modeling efforts in yeast and microalgae. We then point out the gaps in knowledge of lipid metabolism, and enumerate the recent advances of using genome-wide association and quantitative trait loci mapping studies to unravel the genetic regulations of lipid metabolism. Finally, we offer a perspective on how advances in the constraint-based modeling framework can propel further characterization of plant lipid metabolism and its rational manipulation.

Dynamic Metabolic Analysis of Cupriavidus necator DSM545 Producing Poly(3-hydroxybutyric acid) from Glycerol

Cupriavidus necator DSM 545 can utilise glycerol to synthesise poly(3-hydroxybutyric acid) under unbalanced growth conditions, i.e., nitrogen limitation. To improve poly(3-hydroxybutyric acid) (PHB) batch production by C. necator through model-guided bioprocessing or genetic engineering, insights into the dynamic effect of the fermentation conditions on cell metabolism are crucial. In this work, we have used dynamic flux balance analysis (DFBA), a constrained-based stoichiometric modelling approach, to study the metabolic change associated with PHB synthesis during batch cultivation. The model employs the ‘minimisation of all fluxes’ as cellular objectives and measured extracellular fluxes as additional constraints. The mass balance constraints are further adjusted based on thermodynamic considerations. The resultant flux distribution profiles characterise the evolution of metabolic states due to adaptation to dynamic extracellular conditions and provide further insights towards improvements that can be implemented to enhance PHB productivity.

QTL mapping of modelled metabolic fluxes reveals gene variants impacting yeast central carbon metabolism

The yeast Saccharomyces cerevisiae is an attractive industrial microorganism for the production of foods and beverages as well as for various bulk and fine chemicals, such as biofuels or fragrances. Building blocks for these biosyntheses are intermediates of yeast central carbon metabolism (CCM), whose intracellular availability depends on balanced single reactions that form metabolic fluxes. Therefore, efficient product biosynthesis is influenced by the distribution of these fluxes. We recently demonstrated great variations in CCM fluxes between yeast strains of different origins. However, we have limited understanding of flux modulation and the genetic basis of flux variations. In this study, we investigated the potential of quantitative trait locus (QTL) mapping to elucidate genetic variations responsible for differences in metabolic flux distributions (fQTL). Intracellular metabolic fluxes were estimated by constraint-based modelling and used as quantitative phenotypes, and differences in fluxes were linked to genomic variations. Using this approach, we detected four fQTLs that influence metabolic pathways. The molecular dissection of these QTLs revealed two allelic gene variants, PDB1 and VID30, contributing to flux distribution. The elucidation of genetic determinants influencing metabolic fluxes, as reported here for the first time, creates new opportunities for the development of strains with optimized metabolite profiles for various applications.

Quantitative Physiology of Non-Energy-Limited Retentostat Cultures of Saccharomyces cerevisiae at Near-Zero Specific Growth Rates

So far, the physiology of Saccharomyces cerevisiae at near-zero growth rates has been studied in retentostat cultures with a growth-limiting supply of the carbon and energy source. Despite its relevance in nature and industry, the near-zero growth physiology of S. cerevisiae under conditions where growth is limited by the supply of non-energy substrates remains largely unexplored. This study analyzes the physiology of S. cerevisiae in aerobic chemostat and retentostat cultures grown under either ammonium or phosphate limitation. To compensate for loss of extracellular nitrogen- or phosphorus-containing compounds, establishing near-zero growth rates (μ < 0.002 h^-1) in these retentostats required addition of low concentrations of ammonium or phosphate to reservoir media. In chemostats as well as in retentostats, strongly reduced cellular contents of the growth-limiting element (nitrogen or phosphorus) and high accumulation levels of storage carbohydrates were observed. Even at near-zero growth rates, culture viability in non-energy-limited retentostats remained above 80% and ATP synthesis was still sufficient to maintain an adequate energy status and keep cells in a metabolically active state. Compared to similar glucose-limited retentostat cultures, the nitrogen- and phosphate-limited cultures showed aerobic fermentation and a partial uncoupling of catabolism and anabolism. The possibility to achieve stable, near-zero growth cultures of S. cerevisiae under nitrogen or phosphorus limitation offers interesting prospects for high-yield production of bio-based chemicals.IMPORTANCE The yeast Saccharomyces cerevisiae is a commonly used microbial host for production of various biochemical compounds. From a physiological perspective, biosynthesis of these compounds competes with biomass formation in terms of carbon and/or energy equivalents. Fermentation processes functioning at extremely low or near-zero growth rates would prevent loss of feedstock to biomass production. Establishing S. cerevisiae cultures in which growth is restricted by the limited supply of a non-energy substrate therefore could have a wide range of industrial applications but remains largely unexplored. In this work we accomplished near-zero growth of S. cerevisiae through limited supply of a non-energy nutrient, namely, the nitrogen or phosphorus source, and carried out a quantitative physiological study of the cells under these conditions. The possibility to achieve near-zero-growth S. cerevisiae cultures through limited supply of a non-energy nutrient may offer interesting prospects to develop novel fermentation processes for high-yield production of bio-based chemicals.

Fast uncertainty quantification for dynamic flux balance analysis using non-smooth polynomial chaos expansions

We present a novel surrogate modeling method that can be used to accelerate the solution of uncertainty quantification (UQ) problems arising in nonlinear and non-smooth models of biological systems. In particular, we focus on dynamic flux balance analysis (DFBA) models that couple intracellular fluxes, found from the solution of a constrained metabolic network model of the cellular metabolism, to the time-varying nature of the extracellular substrate and product concentrations. DFBA models are generally computationally expensive and present unique challenges to UQ, as they entail dynamic simulations with discrete events that correspond to switches in the active set of the solution of the constrained intracellular model. The proposed non-smooth polynomial chaos expansion (nsPCE) method is an extension of traditional PCE that can effectively capture singularities in the DFBA model response due to the occurrence of these discrete events. The key idea in nsPCE is to use a model of the singularity time to partition the parameter space into two elements on which the model response behaves smoothly. Separate PCE models are then fit in both elements using a basis-adaptive sparse regression approach that is known to scale well with respect to the number of uncertain parameters. We demonstrate the effectiveness of nsPCE on a DFBA model of an E. coli monoculture that consists of 1075 reactions and 761 metabolites. We first illustrate how traditional PCE is unable to handle problems of this level of complexity. We demonstrate that over 800-fold savings in computational cost of uncertainty propagation and Bayesian estimation of parameters in the substrate uptake kinetics can be achieved by using the nsPCE surrogates in place of the full DFBA model simulations. We then investigate the scalability of the nsPCE method by utilizing it for global sensitivity analysis and maximum a posteriori estimation in a synthetic metabolic network problem with a larger number of parameters related to both intracellular and extracellular quantities.

Metabolism of sucrose in a non-fermentative Escherichia coli under oxygen limitation

Biotechnological industry strives to develop anaerobic bioprocesses fueled by abundant and cheap carbon sources, like sucrose. However, oxygen-limiting conditions often lead to by-product formation and reduced ATP yields. While by-product formation is typically decreased by gene deletion, the breakdown of oligosaccharides with inorganic phosphate instead of water could increment the ATP yield. To observe the effect of oxygen limitation during sucrose consumption, a non-fermentative Escherichia coli K-12 strain was transformed with genes enabling sucrose assimilation. It was observed that the combined deletion of the genes adhE, adhP, mhpF, ldhA, and pta abolished the anaerobic growth using sucrose. Therefore, the biomass-specific conversion rates were obtained using oxygen-limited continuous cultures. Strains performing the breakdown of the sucrose by hydrolysis (SUC-HYD) or phosphorolysis (SUC-PHOSP) were studied in such conditions. An experimentally validated in silico model, modified to account for plasmid and protein burdens, was employed to calculate carbon and electron consistent conversion rates. In both strains, the biomass yields were lower than expected and, strikingly, SUC-PHOSP showed a yield lower than SUC-HYD. Flux balance analyses indicated a significant increase in the non-growth-associated ATP expenses by comparison with the growth on glucose. The observed fructose-1,6-biphosphatase and phosphoglucomutase activities, as well as the concentrations of glycogen, suggest the operation of ATP futile cycles triggered by a combination of the oxygen limitation and the metabolites released during the sucrose breakdown.

Fermentation of Saccharomyces cerevisiae - Combining kinetic modeling and optimization techniques points out avenues to effective process design

A combined experimental/theoretical approach is presented, for improving the predictability of Saccharomyces cerevisiae fermentations. In particular, a mathematical model was developed explicitly taking into account the main mechanisms of the fermentation process, allowing for continuous computation of key process variables, including the biomass concentration and the respiratory quotient (RQ). For model calibration and experimental validation, batch and fed-batch fermentations were carried out. Comparison of the model-predicted biomass concentrations and RQ developments with the corresponding experimentally recorded values shows a remarkably good agreement for both batch and fed-batch processes, confirming the adequacy of the model. Furthermore, sensitivity studies were performed, in order to identify model parameters whose variations have significant effects on the model predictions: our model responds with significant sensitivity to the variations of only six parameters. These studies provide a valuable basis for model reduction, as also demonstrated in this paper. Finally, optimization-based parametric studies demonstrate how our model can be utilized for improving the efficiency of Saccharomyces cerevisiae fermentations.

Beneficial Mutations from Evolution Experiments Increase Rates of Growth and Fermentation

A major goal of evolutionary biology is to understand how beneficial mutations translate into increased fitness. Here, we study beneficial mutations that arise in experimental populations of yeast evolved in glucose-rich media. We find that fitness increases are caused by enhanced maximum growth rate (R) that come at the cost of reduced yield (K). We show that for some of these mutants, high R coincides with higher rates of ethanol secretion, suggesting that higher growth rates are due to an increased preference to utilize glucose through the fermentation pathway, instead of respiration. We examine the performance of mutants across gradients of glucose and nitrogen concentrations and show that the preference for fermentation over respiration is influenced by the availability of glucose and nitrogen. Overall, our data show that selection for high growth rates can lead to an enhanced Crabtree phenotype by the way of beneficial mutations that permit aerobic fermentation at a greater range of glucose concentrations.

Dynamic flux balance analysis with nonlinear objective function

Dynamic flux balance analysis (DFBA) extends flux balance analysis and enables the combined simulation of both intracellular and extracellular environments of microbial cultivation processes. A DFBA model contains two coupled parts, a dynamic part at the upper level (extracellular environment) and an optimization part at the lower level (intracellular environment). Both parts are coupled through substrate uptake and product secretion rates. This work proposes a Karush-Kuhn-Tucker condition based solution approach for DFBA models, which have a nonlinear objective function in the lower-level part. To solve this class of DFBA models an extreme-ray-based reformulation is proposed to ensure certain regularity of the lower-level optimization problem. The method is introduced by utilizing two simple example networks and then applied to a realistic model of central carbon metabolism of wild-type Corynebacterium glutamicum.

Interaction of storage carbohydrates and other cyclic fluxes with central metabolism: A quantitative approach by non-stationary 13C metabolic flux analysis

13C labeling experiments in aerobic glucose limited cultures of Saccharomyces cerevisiae at four different growth rates (0.054; 0.101, 0.207, 0.307 h-1) are used for calculating fluxes that include intracellular cycles (e.g., storage carbohydrate cycles, exchange fluxes with amino acids), which are rearranged depending on the growth rate. At low growth rates the impact of the storage carbohydrate recycle is relatively more significant than at high growth rates due to a higher concentration of these materials in the cell (up to 560-fold) and higher fluxes relative to the glucose uptake rate (up to 16%). Experimental observations suggest that glucose can be exported to the extracellular space, and that its source is related to storage carbohydrates, most likely via the export and subsequent extracellular breakdown of trehalose. This hypothesis is strongly supported by 13C-labeling experimental data, measured extracellular trehalose, and the corresponding flux estimations.

13C metabolic flux analysis at a genome-scale

Metabolic models used in 13C metabolic flux analysis generally include a limited number of reactions primarily from central metabolism. They typically omit degradation pathways, complete cofactor balances, and atom transition contributions for reactions outside central metabolism. This study addresses the impact on prediction fidelity of scaling-up mapping models to a genome-scale. The core mapping model employed in this study accounts for (75 reactions and 65 metabolites) primarily from central metabolism. The genome-scale metabolic mapping model (GSMM) (697 reaction and 595 metabolites) is constructed using as a basis the iAF1260 model upon eliminating reactions guaranteed not to carry flux based on growth and fermentation data for a minimal glucose growth medium. Labeling data for 17 amino acid fragments obtained from cells fed with glucose labeled at the second carbon was used to obtain fluxes and ranges. Metabolic fluxes and confidence intervals are estimated, for both core and genome-scale mapping models, by minimizing the sum of square of differences between predicted and experimentally measured labeling patterns using the EMU decomposition algorithm. Overall, we find that both topology and estimated values of the metabolic fluxes remain largely consistent between core and GSM model. Stepping up to a genome-scale mapping model leads to wider flux inference ranges for 20 key reactions present in the core model. The glycolysis flux range doubles due to the possibility of active gluconeogenesis, the TCA flux range expanded by 80% due to the availability of a bypass through arginine consistent with labeling data, and the transhydrogenase reaction flux was essentially unresolved due to the presence of as many as five routes for the inter-conversion of NADPH to NADH afforded by the genome-scale model. By globally accounting for ATP demands in the GSMM model the unused ATP decreased drastically with the lower bound matching the maintenance ATP requirement. A non-zero flux for the arginine degradation pathway was identified to meet biomass precursor demands as detailed in the iAF1260 model. Inferred ranges for 81% of the reactions in the genome-scale metabolic (GSM) model varied less than one-tenth of the basis glucose uptake rate (95% confidence test). This is because as many as 411 reactions in the GSM are growth coupled meaning that the single measurement of biomass formation rate locks the reaction flux values. This implies that accurate biomass formation rate and composition are critical for resolving metabolic fluxes away from central metabolism and suggests the importance of biomass composition (re)assessment under different genetic and environmental backgrounds. In addition, the loss of information associated with mapping fluxes from MFA on a core model to a GSM model is quantified.

Systems modeling approaches for microbial community studies: from metagenomics to inference of the community structure

Microbial communities play important roles in health, industrial applications and earth's ecosystems. With current molecular techniques we can characterize these systems in unprecedented detail. However, such methods provide little mechanistic insight into how the genetic properties and the dynamic couplings between individual microorganisms give rise to their dynamic activities. Neither do they give insight into what we call “the community state”, that is the fluxes and concentrations of nutrients within the community. This knowledge is a prerequisite for rational control and intervention in microbial communities. Therefore, the inference of the community structure from experimental data is a major current challenge. We will argue that this inference problem requires mathematical models that can integrate heterogeneous experimental data with existing knowledge. We propose that two types of models are needed. Firstly, mathematical models that integrate existing genomic, physiological, and physicochemical information with metagenomics data so as to maximize information content and predictive power. This can be achieved with the use of constraint-based genome-scale stoichiometric modeling of community metabolism which is ideally suited for this purpose. Next, we propose a simpler coarse-grained model, which is tailored to solve the inference problem from the experimental data. This model unambiguously relate to the more detailed genome-scale stoichiometric models which act as heterogeneous data integrators. The simpler inference models are, in our opinion, key to understanding microbial ecosystems, yet until now, have received remarkably little attention. This has led to the situation where the modeling of microbial communities, using only genome-scale models is currently more a computational, theoretical exercise than a method useful to the experimentalist.

Applications of computational modeling in metabolic engineering of yeast

Generally, a microorganism's phenotype can be described by its pattern of metabolic fluxes. Although fluxes cannot be measured directly, inference of fluxes is well established. In biotechnology the aim is often to increase the capacity of specific fluxes. For this, metabolic engineering methods have been developed and applied extensively. Many of these rely on balancing of intracellular metabolites, redox, and energy fluxes, using genome-scale models (GEMs) that in combination with appropriate objective functions and constraints can be used to predict potential gene targets for obtaining a preferred flux distribution. These methods point to strategies for altering gene expression; however, fluxes are often controlled by post-transcriptional events. Moreover, GEMs are usually not taking into account metabolic regulation, thermodynamics and enzyme kinetics. To facilitate metabolic engineering, tools from synthetic biology have emerged, enabling integration and assembly of naturally nonexistent, but well-characterized components into a living organism. To describe these systems kinetic models are often used and to integrate these systems with the standard metabolic engineering approach, it is necessary to expand the modeling of metabolism to consider kinetics of individual processes. This review will give an overview about models available for metabolic engineering of yeast and discusses their applications.

Oxidative phosphorylation revisited

The fundamentals of oxidative phosphorylation and photophosphorylation are revisited. New experimental data on the involvement of succinate and malate anions respectively in oxidative phosphorylation and photophosphorylation are presented. These new data offer a novel molecular mechanistic explanation for the energy coupling and ATP synthesis carried out in mitochondria and chloroplast thylakoids. The mechanism does not suffer from the flaws in Mitchell's chemiosmotic theory that have been pointed out in many studies since its first appearance 50 years ago, when it was hailed as a ground-breaking mechanistic explanation of what is perhaps the most important process in cellular energetics. The new findings fit very well with the predictions of Nath's torsional mechanism of energy transduction and ATP synthesis. It is argued that this mechanism, based on at least 15 years of experimental and theoretical work by Sunil Nath, constitutes a fundamentally different theory of the energy conversion process that eliminates all the inconsistencies in Mitchell's chemiosmotic theory pointed out by other authors. It is concluded that the energy-transducing complexes in oxidative phosphorylation and photosynthesis are proton-dicarboxylic acid anion cotransporters and not simply electrogenic proton translocators. These results necessitate revision of previous theories of biological energy transduction, coupling, and ATP synthesis. The novel molecular mechanism is extended to cover ATP synthesis in prokaryotes, in particular to alkaliphilic and haloalkaliphilic bacteria, essentially making it a complete theory addressing mechanistic, kinetic, and thermodynamic details. Finally, based on the new interpretation of oxidative phosphorylation, quantitative values for the P/O ratio, the amount of ATP generated per redox package of the reduced substrates, are calculated and compared with experimental values for fermentation on different substrates. It is our hope that the presentation of oxidative phosphorylation and photophosphorylation from a wholly new perspective will rekindle scientific discussion of a key process in bioenergetics and catalyze new avenues of research in a truly interdisciplinary field.

An evolutionary perspective on the Crabtree effect

The capability to ferment sugars into ethanol is a key metabolic trait of yeasts. Crabtree-positive yeasts use fermentation even in the presence of oxygen, where they could, in principle, rely on the respiration pathway. This is surprising because fermentation has a much lower ATP yield than respiration (2 ATP vs. approximately 18 ATP per glucose). While genetic events in the evolution of the Crabtree effect have been identified, the selective advantages provided by this trait remain controversial. In this review we analyse explanations for the emergence of the Crabtree effect from an evolutionary and game-theoretical perspective. We argue that an increased rate of ATP production is likely the most important factor behind the emergence of the Crabtree effect.

Impact of stoichiometry representation on simulation of genotype-phenotype relationships in metabolic networks

Genome-scale metabolic networks provide a comprehensive structural framework for modeling genotype-phenotype relationships through flux simulations. The solution space for the metabolic flux state of the cell is typically very large and optimization-based approaches are often necessary for predicting the active metabolic state under specific environmental conditions. The objective function to be used in such optimization algorithms is directly linked with the biological hypothesis underlying the model and therefore it is one of the most relevant parameters for successful modeling. Although linear combination of selected fluxes is widely used for formulating metabolic objective functions, we show that the resulting optimization problem is sensitive towards stoichiometry representation of the metabolic network. This undesirable sensitivity leads to different simulation results when using numerically different but biochemically equivalent stoichiometry representations and thereby makes biological interpretation intrinsically subjective and ambiguous. We hereby propose a new method, Minimization of Metabolites Balance (MiMBl), which decouples the artifacts of stoichiometry representation from the formulation of the desired objective functions, by casting objective functions using metabolite turnovers rather than fluxes. By simulating perturbed metabolic networks, we demonstrate that the use of stoichiometry representation independent algorithms is fundamental for unambiguously linking modeling results with biological interpretation. For example, MiMBl allowed us to expand the scope of metabolic modeling in elucidating the mechanistic basis of several genetic interactions in Saccharomyces cerevisiae.

One of the challenging tasks in systems biology is to quantitatively predict the metabolic behavior of the cell under given genetic and environmental constraints. To this end, genome-scale metabolic reconstructions and simulation tools are indispensable. The choice of the objective function to be used for simulating genome-scale metabolic models is dependent on the biological context and one of the most relevant parameters for successful modeling. Formulation of the intended objective function often requires the use of multiple fluxes, e.g. the sum of fluxes through ATP-producing reactions. We demonstrate that the existing tools confound biological interpretation of the simulations due to undesired dependence on the representation of stoichiometry and propose a new tool – Minimization of Metabolites Balance (MiMBl). MiMBl allows casting of the desired biological objective functions into linear optimization models and gives consistent simulation results when using numerically different but biochemically equivalent stoichiometry representations. We demonstrate relevance of MiMBl for addressing biological questions through improved predictions of genetic interactions within the yeast metabolic network. Genetic interactions imply functional relationship between the genes and therefore allow assessing different hypotheses for the underlying biological principles. MiMBl explains several of the genetic interactions as outcome of flux re-routing for minimal metabolite turnover adjustments.

Metabolic engineering of the purine biosynthetic pathway in Corynebacterium glutamicum results in increased intracellular pool sizes of IMP and hypoxanthine

Background

Purine nucleotides exhibit various functions in cellular metabolism. Besides serving as building blocks for nucleic acid synthesis, they participate in signaling pathways and energy metabolism. Further, IMP and GMP represent industrially relevant biotechnological products used as flavor enhancing additives in food industry. Therefore, this work aimed towards the accumulation of IMP applying targeted genetic engineering of Corynebacterium glutamicum.

Results

Blocking of the degrading reactions towards AMP and GMP lead to a 45-fold increased intracellular IMP pool of 22 μmol g_CDW^-1. Deletion of the pgi gene encoding glucose 6-phosphate isomerase in combination with the deactivated AMP and GMP generating reactions, however, resulted in significantly decreased IMP pools (13 μmol g_CDW^-1). Targeted metabolite profiling of the purine biosynthetic pathway further revealed a metabolite shift towards the formation of the corresponding nucleobase hypoxanthine (102 μmol g_CDW^-1) derived from IMP degradation.

Conclusions

The purine biosynthetic pathway is strongly interconnected with various parts of the central metabolism and therefore tightly controlled. However, deleting degrading reactions from IMP to AMP and GMP significantly increased intracellular IMP levels. Due to the complexity of this pathway further degradation from IMP to the corresponding nucleobase drastically increased suggesting additional targets for future strain optimization.

Increased lysine production by flux coupling of the tricarboxylic acid cycle and the lysine biosynthetic pathway--metabolic engineering of the availability of succinyl-CoA in Corynebacterium glutamicum

In this study, we demonstrate increased lysine production by flux coupling using the industrial work horse bacterium Corynebacterium glutamicum, which was mediated by the targeted interruption of the tricarboxylic acid (TCA) cycle at the level of succinyl-CoA synthetase. The succinylase branch of the lysine production pathway functions as the bridging reaction to convert succinyl-CoA to succinate in this aerobic bacterium. The mutant C. glutamicum ΔsucCD showed a 60% increase in the yield of lysine when compared to the advanced lysine producer which was used as parent strain. This mutant was highly vital and exhibited only a slightly reduced specific growth rate. Metabolic flux analysis with (13)C isotope studies confirmed that the increase in lysine production was mediated by pathway coupling. The novel strain exhibited an exceptional flux profile, which was closer to the optimum performance predicted by in silico pathway analysis than to the large set of lysine-producing strains analyzed thus far. Fluxomics and transcriptomics were applied as further targets for next-level strain engineering to identify the back-up mechanisms that were activated upon deletion of the enzyme in the mutant strain. It seemed likely that the cells partly recruited the glyoxylate shunt as a by-pass route. Additionally, the α-ketoglutarate decarboxylase pathway emerged as the potential compensation mechanism. This novel strategy appears equally promising for Escherichia coli, which is used in the industrial production of lysine, wherein this bacterium synthesizes lysine exclusively by succinyl-CoA activation of pathway intermediates. The channeling of a high flux pathway into a production pathway by pathway coupling is an interesting metabolic engineering strategy that can be explored to optimize bio-production in the future.

Understanding regulation of metabolism through feasibility analysis

Understanding cellular regulation of metabolism is a major challenge in systems biology. Thus far, the main assumption was that enzyme levels are key regulators in metabolic networks. However, regulation analysis recently showed that metabolism is rarely controlled via enzyme levels only, but through non-obvious combinations of hierarchical (gene and enzyme levels) and metabolic regulation (mass action and allosteric interaction). Quantitative analyses relating changes in metabolic fluxes to changes in transcript or protein levels have revealed a remarkable lack of understanding of the regulation of these networks. We study metabolic regulation via feasibility analysis (FA). Inspired by the constraint-based approach of Flux Balance Analysis, FA incorporates a model describing kinetic interactions between molecules. We enlarge the portfolio of objectives for the cell by defining three main physiologically relevant objectives for the cell: function, robustness and temporal responsiveness. We postulate that the cell assumes one or a combination of these objectives and search for enzyme levels necessary to achieve this. We call the subspace of feasible enzyme levels the feasible enzyme space. Once this space is constructed, we can study how different objectives may (if possible) be combined, or evaluate the conditions at which the cells are faced with a trade-off among those. We apply FA to the experimental scenario of long-term carbon limited chemostat cultivation of yeast cells, studying how metabolism evolves optimally. Cells employ a mixed strategy composed of increasing enzyme levels for glucose uptake and hexokinase and decreasing levels of the remaining enzymes. This trade-off renders the cells specialized in this low-carbon flux state to compete for the available glucose and get rid of over-overcapacity. Overall, we show that FA is a powerful tool for systems biologists to study regulation of metabolism, interpret experimental data and evaluate hypotheses.

Energy coupling in Saccharomyces cerevisiae: selected opportunities for metabolic engineering

Free-energy (ATP) conservation during product formation is crucial for the maximum product yield that can be obtained, but often overlooked in metabolic engineering strategies. Product pathways that do not yield ATP or even demand input of free energy (ATP) require an additional pathway to supply the ATP needed for product formation, cellular maintenance, and/or growth. On the other hand, product pathways with a high ATP yield may result in excess biomass formation at the expense of the product yield. This mini-review discusses the importance of the ATP yield for product formation and presents several opportunities for engineering free-energy (ATP) conservation, with a focus on sugar-based product formation by Saccharomyces cerevisiae. These engineering opportunities are not limited to the metabolic flexibility within S. cerevisiae itself, but also expression of heterologous reactions will be taken into account. As such, the diversity in microbial sugar uptake and phosphorylation mechanisms, carboxylation reactions, product export, and the flexibility of oxidative phosphorylation via the respiratory chain and H(+) -ATP synthase can be used to increase or decrease free-energy (ATP) conservation. For product pathways with a negative, zero or too high ATP yield, analysis and metabolic engineering of the ATP yield of product formation will provide a promising strategy to increase the product yield and simplify process conditions.

Quantification of metabolism in Saccharomyces cerevisiae under hyperosmotic conditions using elementary mode analysis

Yeast metabolism under hyperosmotic stress conditions was quantified using elementary mode analysis to obtain insights into the metabolic status of the cell. The fluxes of elementary modes were determined as solutions to a linear program that used the stoichiometry of the elementary modes as constraints. The analysis demonstrated that distinctly different sets of elementary modes operate under normal and hyperosmotic conditions. During the adaptation phase, elementary modes that only produce glycerol are active, while elementary modes that yield biomass, ethanol, and glycerol become active after the adaptive phase. The flux distribution in the metabolic network, calculated using the fluxes in the elementary modes, was employed to obtain the flux ratio at key nodes. At the glucose 6-phosphate (G6P) node, 25% of the carbon influx was diverted towards the pentose phosphate pathway under normal growth conditions, while only 0.3% of the carbon flux was diverted towards the pentose phosphate pathway during growth at 1 M NaCl, indicating that cell growth is arrested under hyperosmotic conditions. Further, objective functions were used in the linear program to obtain optimal solution spaces corresponding to the different accumulation rates. The analysis demonstrated that while biomass formation was optimal under normal growth conditions, glycerol synthesis was closer to optimal during adaptation to osmotic shock.

Laboratory evolution of new lactate transporter genes in a jen1Δ mutant of Saccharomyces cerevisiae and their identification as ADY2 alleles by whole-genome resequencing and transcriptome analysis

Laboratory evolution is a powerful approach in applied and fundamental yeast research, but complete elucidation of the molecular basis of evolved phenotypes remains a challenge. In this study, DNA microarray-based transcriptome analysis and whole-genome resequencing were used to investigate evolution of novel lactate transporters in Saccharomyces cerevisiae that can replace Jen1p, the only documented S. cerevisiae lactate transporter. To this end, a jen1Δ mutant was evolved for growth on lactate in serial batch cultures. Two independent evolution experiments yielded growth on lactate as sole carbon source (0.14 and 0.18 h(-1) , respectively). Transcriptome analysis did not provide leads, but whole-genome resequencing showed different single-nucleotide changes (C755G/Leu219Val and C655G/Ala252Gly) in the acetate transporter gene ADY2. Introduction of these ADY2 alleles in a jen1Δ ady2Δ strain enabled growth on lactate (0.14 h(-1) for Ady2p(Leu219Val) and 0.12 h(-1) for Ady2p(Ala252Gly) ), demonstrating that these alleles of ADY2 encode efficient lactate transporters. Depth of coverage of DNA sequencing, combined with karyotyping, gene deletions and diagnostic PCR, showed that an isochromosome III (c. 475 kb) with two additional copies of ADY2(C755G) had been formed via crossover between retrotransposons YCLWΔ15 and YCRCΔ6. The isochromosome formation shows how even short periods of selective pressure can cause substantial karyotype changes.

Metabolic flux analysis and visualization

One of the ultimate goals of systems biology research is to obtain a comprehensive understanding of the control mechanisms of complex cellular metabolisms. Metabolic Flux Analysis (MFA) is a important method for the quantitative estimation of intracellular metabolic flows through metabolic pathways and the elucidation of cellular physiology. The primary challenge in the use of MFA is that many biological networks are underdetermined systems; it is therefore difficult to narrow down the solution space from the stoichiometric constraints alone. In this tutorial, we present an overview of Flux Balance Analysis (FBA) and (13)C-Metabolic Flux Analysis ((13)C-MFA), both of which are frequently used to solve such underdetermined systems, and we demonstrate FBA and (13)C-MFA using the genome-scale model and the central carbon metabolism model, respectively. Furthermore, because such comprehensive study of intracellular fluxes is inherently complex, we subsequently introduce various pathway mapping and visualization tools to facilitate understanding of these data in the context of the pathways. Specific visualization of MFA results using the BioCyc Omics Viewer and Pathway Projector are shown as illustrative examples.

Fifteen years of large scale metabolic modeling of yeast: developments and impacts

Since the first large-scale reconstruction of the Saccharomyces cerevisiae metabolic network 15 years ago the development of yeast metabolic models has progressed rapidly, resulting in no less than nine different yeast genome-scale metabolic models. Here we review the historical development of large-scale mathematical modeling of yeast metabolism and the growing scope and impact of applications of these models in four different areas: as guide for metabolic engineering and strain improvement, as a tool for biological interpretation and discovery, applications of novel computational framework and for evolutionary studies.

Potentials and limitations of miniaturized calorimeters for bioprocess monitoring

In theory, heat production rates are very well suited for analysing and controlling bioprocesses on different scales from a few nanolitres up to many cubic metres. Any bioconversion is accompanied by a production (exothermic) or consumption (endothermic) of heat. The heat is tightly connected with the stoichiometry of the bioprocess via the law of Hess, and its rate is connected to the kinetics of the process. Heat signals provide real-time information of bioprocesses. The combination of heat measurements with respirometry is theoretically suited for the quantification of the coupling between catabolic and anabolic reactions. Heat measurements have also practical advantages. Unlike most other biochemical sensors, thermal transducers can be mounted in a protected way that prevents fouling, thereby minimizing response drifts. Finally, calorimetry works in optically opaque solutions and does not require labelling or reactants. It is surprising to see that despite all these advantages, calorimetry has rarely been applied to monitor and control bioprocesses with intact cells in the laboratory, industrial bioreactors or ecosystems. This review article analyses the reasons for this omission, discusses the additional information calorimetry can provide in comparison with respirometry and presents miniaturization as a potential way to overcome some inherent weaknesses of conventional calorimetry. It will be discussed for which sample types and scientific question miniaturized calorimeter can be advantageously applied. A few examples from different fields of microbiological and biotechnological research will illustrate the potentials and limitations of chip calorimetry. Finally, the future of chip calorimetry is addressed in an outlook.

Pentose phosphate pathway flux analysis for glycopeptide antibiotic vancomycin production during glucose-limited cultivation of Amycolatopsis orientalis

In vivo pentose phosphate pathway (PPP) enzymes such as glucose-6-phosphate dehydrogenase (G6PDH), 6-phosphogluconate dehydrogenase (6PGDH), and transaldolase (TAL) activities as well as ATP- and ADP-level variations of Amycolatopsis orientalis were investigated with respect to glucose concentration and incubation period. G6PDH, 6PGDH, and TAL activities of A. orientalis reached maximum levels at 48 hr for all glucose concentrations used, after which the levels began to decline. G6PDH, 6PGDH, and TAL activities showed positive correlation with the glucose concentration up to 15 g/L, while further increases had an opposite effect. Intracellular ATP level showed a positive correlation with glucose concentrations, while ADP level increased up to 15 g/L. ATP concentration of A. orientalis increased rapidly at 48 hr of incubation, as was the case also for G6PDH, 6PGDH, and TAL activities, although the incubation period corresponding to maximum values of ADP shifted to 60 hr. Production of the glycopeptide antibiotic vancomycin increased with the increases in glucose concentrations up to 15 g/L, by showing coherence in the rates of oxidative and nonoxidative parts of the PPP.

Toward Preclinical Predictive Drug Testing for Metabolism and Hepatotoxicity by Using In Vitro Models Derived from Human Embryonic Stem Cells and Human Cell Lines — A Report on the Vitrocellomics EU-project

Drug-induced liver injury is a common reason for drug attrition in late clinical phases, and even for post-launch withdrawals. As a consequence, there is a broad consensus in the pharmaceutical industry, and within regulatory authorities, that a significant improvement of the current in vitro test methodologies for accurate assessment and prediction of such adverse effects is needed. For this purpose, appropriate in vivo-like hepatic in vitro models are necessary, in addition to novel sources of human hepatocytes. In this report, we describe recent and ongoing research toward the use of human embryonic stem cell (hESC)-derived hepatic cells, in conjunction with new and improved test methods, for evaluating drug metabolism and hepatotoxicity. Recent progress on the directed differentiation of human embryonic stem cells to the functional hepatic phenotype is reported, as well as the development and adaptation of bioreactors and toxicity assay technologies for the testing of hepatic cells. The aim of achieving a testing platform for metabolism and hepatotoxicity assessment, based on hESC-derived hepatic cells, has advanced markedly in the last 2–3 years. However, great challenges still remain, before such new test systems could be routinely used by the industry. In particular, we give an overview of results from the Vitrocellomics project (EU Framework 6) and discuss these in relation to the current state-of-the-art and the remaining difficulties, with suggestions on how to proceed before such in vitro systems can be implemented in industrial discovery and development settings and in regulatory acceptance.

Genome-scale metabolic models of Saccharomyces cerevisiae

Systematic analysis of Saccharomyces cerevisiae metabolic functions and pathways has been the subject of extensive studies and established in many aspects. With the reconstruction of the yeast genome-scale metabolic (GSM) network and in silico simulation of the GSM model, the nature of the underlying cellular processes can be tested and validated with the increasing metabolic knowledge. GSM models are also being exploited in fundamental research studies and industrial applications. In this chapter, the principle concepts for construction, simulation and validation of GSM models, progressive applications of the yeast GSM models, and future perspectives are described. This will support and encourage researchers who are interested in systemic analysis of yeast metabolism and systems biology.

Phenotypic characterization of Corynebacterium glutamicum using elementary modes towards synthesis of amino acids

Elementary flux mode (EFM) analysis is a powerful tool to represent the metabolic network structure and can be further utilized for flux analysis. The method enables characterization and quantification of feasible phenotypes in microbes. EFM analysis was employed to characterize the phenotype of Corynebacterium glutamicum to yield various amino acids. The metabolic network of C. glutamicum yielded 62 elementary modes by incorporating the accumulation of amino acids namely, lysine, alanine, valine, glutamine and glutamate. The analysis also allowed us to compute the maximum theoretical yield for the synthesis of various amino acids. These 62 elementary modes were further used to obtain optimal phenotypic space towards accumulation of biomass and lysine. The study indicated that the optimal solution space from 62 elementary modes forms a super space which incorporates various mutants including lysine producing strain of C. glutamicum. The analysis was also extended to obtain sensitivity of the network to variation in the stoichiometry of NADP in the definition of biomass.

ELECTRONIC SUPPLEMENTARY MATERIAL

The online version of this article (doi:10.1007/s11693-011-9073-8) contains supplementary material, which is available to authorized users.

Genome-derived minimal metabolic models for Escherichia coli MG1655 with estimated in vivo respiratory ATP stoichiometry

Metabolic network models describing growth of Escherichia coli on glucose, glycerol and acetate were derived from a genome scale model of E. coli. One of the uncertainties in the metabolic networks is the exact stoichiometry of energy generating and consuming processes. Accurate estimation of biomass and product yields requires correct information on the ATP stoichiometry. The unknown ATP stoichiometry parameters of the constructed E. coli network were estimated from experimental data of eight different aerobic chemostat experiments carried out with E. coli MG1655, grown at different dilution rates (0.025, 0.05, 0.1, and 0.3 h(-1)) and on different carbon substrates (glucose, glycerol, and acetate). Proper estimation of the ATP stoichiometry requires proper information on the biomass composition of the organism as well as accurate assessment of net conversion rates under well-defined conditions. For this purpose a growth rate dependent biomass composition was derived, based on measurements and literature data. After incorporation of the growth rate dependent biomass composition in a metabolic network model, an effective P/O ratio of 1.49 +/- 0.26 mol of ATP/mol of O, K(X) (growth dependent maintenance) of 0.46 +/- 0.27 mol of ATP/C-mol of biomass and m(ATP) (growth independent maintenance) of 0.075 +/- 0.015 mol of ATP/C-mol of biomass/h were estimated using a newly developed Comprehensive Data Reconciliation (CDR) method, assuming that the three energetic parameters were independent of the growth rate and the used substrate. The resulting metabolic network model only requires the specific rate of growth, micro, as an input in order to accurately predict all other fluxes and yields.

Metabolome, transcriptome and metabolic flux analysis of arabinose fermentation by engineered Saccharomyces cerevisiae

One of the challenges in strain improvement by evolutionary engineering is to subsequently determine the molecular basis of the improved properties that were enriched from the natural genetic variation during the selective conditions. This study focuses on Saccharomyces cerevisiae IMS0002 which, after metabolic and evolutionary engineering, ferments the pentose sugar arabinose. Glucose- and arabinose-limited anaerobic chemostat cultures of IMS0002 and its non-evolved ancestor were subjected to transcriptome analysis, intracellular metabolite measurements and metabolic flux analysis. Increased expression of the GAL-regulon and deletion of GAL2 in IMS0002 confirmed that the galactose transporter is essential for growth on arabinose. Elevated intracellular concentrations of pentose-phosphate-pathway intermediates and upregulation of TKL2 and YGR043c (encoding transketolase and transaldolase isoenzymes) suggested an involvement of these genes in flux-controlling reactions in arabinose fermentation. Indeed, deletion of these genes in IMS0002 caused a 21% reduction of the maximum specific growth rate on arabinose.

Redox biocatalysis and metabolism: molecular mechanisms and metabolic network analysis

Whole-cell biocatalysis utilizes native or recombinant enzymes produced by cellular metabolism to perform synthetically interesting reactions. Besides hydrolases, oxidoreductases represent the most applied enzyme class in industry. Oxidoreductases are attributed a high future potential, especially for applications in the chemical and pharmaceutical industries, as they enable highly interesting chemistry (e.g., the selective oxyfunctionalization of unactivated C-H bonds). Redox reactions are characterized by electron transfer steps that often depend on redox cofactors as additional substrates. Their regeneration typically is accomplished via the metabolism of whole-cell catalysts. Traditionally, studies towards productive redox biocatalysis focused on the biocatalytic enzyme, its activity, selectivity, and specificity, and several successful examples of such processes are running commercially. However, redox cofactor regeneration by host metabolism was hardly considered for the optimization of biocatalytic rate, yield, and/or titer. This article reviews molecular mechanisms of oxidoreductases with synthetic potential and the host redox metabolism that fuels biocatalytic reactions with redox equivalents. The tools discussed in this review for investigating redox metabolism provide the basis for studies aiming at a deeper understanding of the interplay between synthetically active enzymes and metabolic networks. The ultimate goal of rational whole-cell biocatalyst engineering and use for fine chemical production is discussed.